Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Information Security
Published in: International Journal of Information Security 1/2022

19-03-2021 | Regular Contribution

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

Authors: Jean-Paul A. Yaacoub, Hassan N. Noura, Ola Salman, Ali Chehab

Published in: International Journal of Information Security | Issue 1/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The recent digital revolution led robots to become integrated more than ever into different domains such as agricultural, medical, industrial, military, police (law enforcement), and logistics. Robots are devoted to serve, facilitate, and enhance the human life. However, many incidents have been occurring, leading to serious injuries and devastating impacts such as the unnecessary loss of human lives. Unintended accidents will always take place, but the ones caused by malicious attacks represent a very challenging issue. This includes maliciously hijacking and controlling robots and causing serious economic and financial losses. This paper reviews the main security vulnerabilities, threats, risks, and their impacts, and the main security attacks within the robotics domain. In this context, different approaches and recommendations are presented in order to enhance and improve the security level of robotic systems such as multi-factor device/user authentication schemes, in addition to multi-factor cryptographic algorithms. We also review the recently presented security solutions for robotic systems.
previous article Public key versus symmetric key cryptography in client–server authentication protocols
next article Track for surveys

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M.: Industry 4.0: the future of productivity and growth in manufacturing industries. Boston Consult. Group 9(1), 54–89 (2015)
2.
Bahrin, M.A.K., Othman, M.F., Nor Azli, N.H., Talib, M.F.: Industry 4.0: a review on industrial automation and robotic. J. Teknol. 78(6–13), 137–143 (2016)
3.
Pfeiffer, S.: Robots, industry 4.0 and humans, or why assembly work is more than routine work. Societies 6(2), 16 (2016)
4.
Shyvakov, O.: Developing a security framework for robots. Master’s thesis, University of Twente (2017)
5.
Simoens, P., Dragone, M., Saffiotti, A.: The internet of robotic things: a review of concept, added value and applications. Int. J. Adv. Robot. Syst. 15(1), 1729881418759424 (2018)CrossRef
6.
Chui, M., Manyika, J., Miremadi, M.: Where machines could replace humans-and where they can’t (yet). McKinsey Q. 7, 1–6 (2016)
7.
Kirschgens, L.A., Ugarte, I.Z., Uriarte, E.G., Rosas, A.M., Vilches, V.M.: Robot hazards: from safety to security (2018). arXiv preprint arXiv:​1806.​06681
8.
Guerrero-Higueras, Á.M., DeCastro-Garcia, N., Matellan, V.: Detection of cyber-attacks to indoor real time localization systems for autonomous robots. Robot. Auton. Syst. 99, 75–83 (2018)CrossRef
9.
Petit, J., Shladover, S.E.: Potential cyberattacks on automated vehicles. IEEE Trans. Intell. Transp. Syst. 16(2), 546–556 (2015)
10.
Cerrudo, C., Apa, L.: Hacking robots before skynet. Cybersecurity Insight, IOActive Report, Seattle, USA (2017)
11.
Vuong, T., Filippoupolitis, A., Loukas, G., Gan, D.: Physical indicators of cyber attacks against a rescue robot. In: 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 338–343. IEEE (2014)
12.
Dash, P., Karimibiuki, M., Pattabiraman, K.: Stealthy attacks against robotic vehicles protected by control-based intrusion detection techniques. J. Digit. Threats Res. Pract. 2(1), 1–25 (2021)CrossRef
13.
Chowdhury, A., Karmakar, G., Kamruzzaman, J.: Survey of recent cyber security attacks on robotic systems and their mitigation approaches. In: Cyber Law, Privacy, and Security: Concepts, Methodologies, Tools, and Applications, pp. 1426–1441. IGI Global (2019)
14.
Lacava, G., Marotta, A., Martinelli, F., Saracino, A., La Marra, A., Gil-Uriarte, E., Vilches, V.M.: Current research issues on cyber security in robotics (2020)
15.
Mitchell, R., Chen, I.-R.: A survey of intrusion detection techniques for cyber-physical systems. ACM Comput. Surv. (CSUR) 46(4), 55 (2014)CrossRef
16.
Kehoe, B., Patil, S., Abbeel, P., Goldberg, K.: A survey of research on cloud robotics and automation. IEEE Trans. Autom. Sci. Eng. 12(2), 398–409 (2015)CrossRef
17.
Chowdhury, A., Karmakar, G., Kamruzzaman, J.: Survey of recent cyber security attacks on robotic systems and their mitigation approaches. In: Detecting and Mitigating Robotic Cyber Security Risks, pp. 284–299. IGI Global (2017)
18.
Jeong, S.-Y., Choi, I.-J., Kim, Y.-J., Shin, Y.-M., Han, J.-H., Jung, G.-H., Kim, K.-G.: A study on ros vulnerabilities and countermeasure. In: Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human–Robot Interaction, pp. 147–148. ACM (2017)
19.
Hellaoui, H., Koudil, M., Bouabdallah, A.: Energy-efficient mechanisms in security of the internet of things: a survey. Comput. Netw. 127, 173–189 (2017)CrossRef
20.
Guiochet, J., Machin, M., Waeselynck, H.: Safety-critical advanced robots: a survey. Robot. Auton. Syst. 94, 43–52 (2017)CrossRef
21.
Dieber, B., Breiling, B., Taurer, S., Kacianka, S., Rass, S., Schartner, P.: Security for the robot operating system. Robot. Auton. Syst. 98, 192–203 (2017)CrossRef
22.
Alcaraz, C., Cazorla, L., Lopez, J.: Cyber-physical systems for wide-area situational awareness. In: Cyber-Physical Systems, pp. 305–317. Elsevier (2017)
23.
Rubio, J.E., Alcaraz, C., Roman, R., Lopez, J.: Current cyber-defense trends in industrial control systems. Comput. Secur. 87, 101561 (2019)CrossRef
24.
Jahan, F., Sun, W., Niyaz, Q., Alam, M.: Security modeling of autonomous systems: a survey. ACM Comput. Surv. (CSUR) 52(5), 1–34 (2019)CrossRef
25.
Chen, J., Li, K., Zhang, Z., Li, K., Yu, P.S.: A survey on applications of artificial intelligence in fighting against covid-19 (2020). arXiv preprint arXiv:​2007.​02202
26.
Brem, A., Viardot, E., Nylund, P.A.: Implications of the coronavirus (covid-19) outbreak for innovation: which technologies will improve our lives? Technol. Forecast. Soc. Change 163, (2020)CrossRef
27.
Khan, F.N., Khanam, A.A., Ramlal, A., Ahmad, S.: A review on predictive systems and data models for covid-19. In: Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, pp. 123–164. Springer (2020)
28.
Fan, D., Li, Y., Liu, W., Yue, X.-G., Boustras, G.: Weaving public health and safety nets to respond the covid-19 pandemic. Saf. Sci. 134, 105058 (2020)CrossRef
29.
Bokolo Anthony Jnr: Use of telemedicine and virtual care for remote treatment in response to covid-19 pandemic. J. Med. Syst. 44(7), 1–9 (2020)CrossRef
30.
Yaacoub, J.-P.A., Noura, H.N., Salman, O., Chehab, A.: Security analysis of drones systems: attacks, limitations, and recommendations. Internet Things 11, 100218 (2020)CrossRef
31.
Wang, H., Cheng, H., Hao, H.: The use of unmanned aerial vehicle in military operations. In: International Conference on Man–Machine–Environment System Engineering, pp. 939–945. Springer (2020)
32.
Kamel, M.A., Yu, X., Zhang, Y.: Formation control and coordination of multiple unmanned ground vehicles in normal and faulty situations: a review. Annu. Rev. Control 49, 128–144 (2020)MathSciNetCrossRef
33.
Nandyal, A.A., Adithya, D.M., Karthik, K., Manikantan, G., Sudha, P.N.: A literature survey on “unmanned underwater vehicle for monitoring aquatic ecosystem”. Int. J. Eng. Appl. Sci. Technol. 5(2), 599–601 (2020). (ISSN: 2455-2143)
34.
He, Y., Wang, D.B., Ali, Z.A.: A review of different designs and control models of remotely operated underwater vehicle. Meas. Control, p. 0020294020952483 (2020)
35.
Yaacoub, J.-P.A., Salman, O., Noura, H.N., Kaaniche, N., Chehab, A., Malli, M.: Cyber-physical systems security: limitations, issues and future trends. Microprocess. Microsyst. 77, 102019 (2020)CrossRef
36.
Yaacoub, J.P.A., Fernandez, J.H., Noura, H.N., Chehab, A.: Security of power line communication systems: issues, limitations and existing solutions. Comput. Sci. Rev. 39, 100331 (2021)MathSciNetCrossRef
37.
Yaacoub, J.-P.A., Noura, M., Noura, H.N., Salman, O., Yaacoub, E., Couturier, R., Chehab, A.: Securing internet of medical things systems: limitations, issues and recommendations. Future Gener. Comput. Syst. 105, 581–606 (2020)CrossRef
38.
Gogu, G., Ray, P., Neagoe, M., Gogu, G., Diaconescu, D., Pocola, A.G., Pop, D.O., Petra, C.: Robotics and manufacturing. In: Talaba, D., Roche, T. (eds.) Product Engineering: Eco-Design, Technologies and Green Energy, p. 348. Springer, Cham (2006)
39.
Kadir, M.A.: Role of telemedicine in healthcare during covid-19 pandemic in developing countries. Telehealth Med, Today (2020)CrossRef
40.
Beasley, R.A.: Medical robots: current systems and research directions. J. Robot. (2012)
41.
Rosen, J., Hannaford, B.: Doc at a distance. IEEE Spectr. 43(10), 34–39 (2006)CrossRef
42.
Cheein, F.A.A., Carelli, R.: Agricultural robotics: unmanned robotic service units in agricultural tasks. IEEE Ind. Electron. Mag. 7(3), 48–58 (2013)CrossRef
43.
Murphy, R.R., Tadokoro, S., Nardi, D., Jacoff, A., Fiorini, P., Choset, H., Erkmen, A.M.: Search and rescue robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1151–1173. Springer, Berlin (2008)CrossRef
44.
Murphy, R.R., Tadokoro, S., Kleiner, A.: Disaster robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1577–1604. Springer, Berlin (2016)CrossRef
45.
Stager, P.: Visual search capability in search and rescue(sar) (1974)
46.
McKirdy, E.: Thailand cave rescue: boys appear in new video, ‘i am healthy’ (2018)
47.
Naghsh, A.M., Gancet, J., Tanoto, A., Roast, C.: Analysis and design of human–robot swarm interaction in firefighting. In: The 17th IEEE International Symposium on Robot and Human Interactive Communication, 2008. RO-MAN 2008, pp. 255–260. IEEE (2008)
48.
Hong, J.H., Matson, E.T., Taylor, J.M.: Design of knowledge-based communication between human and robot using ontological semantic technology in firefighting domain. In: Robot Intelligence Technology and Applications, vol. 2, pp. 311–325. Springer (2014)
49.
Mansour, H., Bitar, E., Fares, Y., Makdessi, A., Maalouf, A., El Ghoul, M., Mansour, M., Chami, A., Khalil, M., Jalkh, A., et al.: Beirut port ammonium nitrate explosion. SSRN (2020)
50.
Cheaito, M.A., Al-Hajj, S.: A brief report on the beirut port explosion. Mediterr. J. Emerg. Med, Acute Care (2020)
51.
Oxford Analytica. Beirut blast could bring hunger, disease and fury. Emerald Expert Briefings (2020)
52.
Stennett, C., Gaulter, S., Akhavan, J.: An estimate of the TNT-equivalent net explosive quantity (NEQ) of the Beirut port explosion using publicly-available tools and data. Propellants Explos, Pyrotech. 45(11), 1675–1679 (2020)CrossRef
53.
Thielman, S.: Use of police robot to kill Dallas shooting suspect believed to be first in US history. The Guardian (2016)
54.
Ringrose, K., Ramjee, D.: Watch where you walk: law enforcement surveillance and protester privacy. Calif. L. Rev. Online 11, 349 (2020)
55.
Schulte, P.: Future war: Ai, drones, terrorism and counterterror. In: Handbook of Terrorism and Counter Terrorism Post 9/11. Edward Elgar Publishing (2019)
56.
Zych, J.: The use of weaponized kites and balloons in the Israeli-Palestinian conflict. Secur. Def. Q. 27(5), 71–83 (2019)CrossRef
57.
Engberts, B., Gillissen, E.: Policing from above: drone use by the police. In: The Future of Drone Use, pp. 93–113. Springer (2016)
58.
Shachtman, N.: Military stats reveal epicenter of us drone war. Wired. com 9 (2012)
59.
Wilson, C.: Improvised explosive devices in Iraq: effects and countermeasures. In: CRS Report for Congress, Library of Congress Washington DC Congressional Research Service (2005)
60.
Lesley-Dixon, K.: Northern Ireland: the troubles: from the provos to the det, 1968–1998. Pen and Sword (2018)
61.
Miller, D.: Rethinking Northern Ireland: Culture. Ideology and Colonialism. Routledge, London (2014)CrossRef
62.
Krishnan, A.: Killer Robots: Legality and Ethicality of Autonomous Weapons. Routledge, London (2016)CrossRef
63.
Barboza, A.R.: The Irish Republican Army: an examination of imperialism, terror, and just war theory. Master’s thesis, California Polytechnic State University, San Luis Obispo (2020)
64.
Karnozov, V., et al.: Russia and Turkey put their latest equipment to the test in Syria. Def. Rev. Asia 14(2), 20 (2020)
65.
Zoltán, Ő.: Special features of the Russian-Ukrainian armed conflict. Hadmérnök 15(1), 207–220 (2020)CrossRef
66.
Okpaleke, F., Burton, J.: 9 US grand strategy and the use of unmanned aerial vehicles during the George W. Bush administration. In: Emerging Technologies and International Security: Machines, the State, and War, p. 153 (2020)
67.
Scipanov, L.V., Dolceanu, D.: The opportunity for using remotely operated underwater vehicles in support of naval actions. Bull. Carol I Natl. Def. Univ. 9(3), 62–68 (2020)
68.
Siwek, M., Wacławik, K.: Legal aspects of production and operation of autonomous combat robots. Problemy Mechatroniki: uzbrojenie, lotnictwo, inżynieria bezpieczeństwa, 11 (2020)
69.
Thornton, R., Miron, M.: Towards the ‘third revolution in military affairs’ the Russian military’s use of AI-enabled cyber warfare. RUSI J. 165, 1–10 (2020)CrossRef
70.
Abiodun, T.F., Taofeek, C.R.: Unending war on boko haram terror in northeast Nigeria and the need for deployment of military robots or autonomous weapons systems to complement military operations. Journal DOI 6(6) (2020)
71.
Westerheijden, V.R.: Remote warfare comes home: an inquiry in the Dutch government’s development of discourse on airstrikes and drones between 1998–2020. Master’s thesis, Utrecht University (2020)
72.
Oxford Analytica: Uae’s bolstering of Libya’s haftar is a risky policy. Emerald Expert Briefings (oxan-db) (2020)
73.
Milan, F.F., Tabrizi, A.B.: Armed, unmanned, and in high demand: the drivers behind combat drones proliferation in the Middle East. Small Wars Insurgencies 31(4), 730–750 (2020)CrossRef
74.
Gallagher, K.: Killer optics: exports of Wescam sensors to Turkey (2020)
75.
Clark, M., Yazici, E.: Erdogan seeks to upend kremlin-backed status quo in Nagorno-Karabakh. Institute for the Study of War, p. 1 (2020)
76.
Tol, T., et al.: Transitions online\_around the bloc-Tuesday, 27 October 2020. Transitions Online (11/02):9–11 (2020)
77.
Khan, N., Fahad, S., Naushad, M., Faisal, S.: Analysis of Arminia and Azerbijan war and its impact on both countries economies. Available at SSRN 3709329 (2020)
78.
Jenzen-Jones, N.R.: Understanding the threat posed by cots small UAVs armed with CBR payloads. In: 21st Century Prometheus, pp. 179–204. Springer (2020)
79.
Kaya, E.K.: Walking a fragile path: assessing the idlib de-militarization deal (2018)
80.
Sadat, S.A.: Iran ties to the Palestinian Islamic resistance movement with emphasis on the Islamic Jihad Movement (PIJ), pp. 77–105 (2016)
81.
Bendett, S.: Battle robots rivalry and the future of war (2019)
82.
Brookes, P.: The growing Iranian unmanned combat aerial vehicle threat needs us action. Herit. Found. Backgr. 3437 (2019)
83.
Sims, A.: The rising drone threat from terrorists. Georget. J. Int. Aff. 19, 97–107 (2018)CrossRef
84.
Rossiter, A.: Bots on the ground: an impending UGV revolution in military affairs? Small Wars Insurgencies 31(4), 851–873 (2020)CrossRef
85.
Chávez, K., Swed, O.: Off the shelf: the violent nonstate actor drone threat. Air Space Power J. 29, 29 (2020)
86.
Vogiatzis, D.: The way to the promised land or the door to Armageddon: how severe are the threats against the physical security of israeli offshore gas platforms? Naval Postgraduate School, Monterey, CA. Ph.D. thesis (2020)
87.
Borg, S.: Assembling Israeli drone warfare: loitering surveillance and operational sustainability. Security Dialogue, p. 0967010620956796 (2020)
88.
Benjamin, G.: Drone culture: perspectives on autonomy and anonymity. AI & SOCIETY, pp. 1–11 (2020)
89.
Popister, F., Steopan, M., Pusca, A.: Surveillance robot for military use. Acta Tech. Napocensis-Ser. Appl. Math. Mech. Eng. 63(3) (2020)
90.
Fishman, J., Kuperwasser, Y.: Willful blindness and the mistake of underestimation: the Oslo gamble. Natl. Resili. Polit. Soc. 2(1), 9–50 (2020)
91.
Marcus, R.D.: Learning ‘under fire’: Israel’s improvised military adaptation to Hamas tunnel warfare. J. Strateg. Stud. 42(3–4), 344–370 (2019)CrossRef
92.
Michael, K., Dostri, O.: The Hamas military buildup. The crisis of the Gaza strip: a way out (Tel Aviv: INSS, 2017), pp. 49–60 (2019)
93.
White, J.: The combat performance of Hamas in the Gaza war of 2014. CTC Sentin 7(9), 9–13 (2014)
94.
Gillespie, P.G.: Weapons of choice: the development of precision guided munitions. The University of Alabama Press, Tuscaloosa (2006)
95.
Fink, A.H., Wilson, W.A., Holte, R.T.: System and methods for countering satellite-navigated munitions, December 20 2016. US Patent 9,523,773
96.
Ahner, D., McCarthy, A.: Response surface modeling of precision-guided fragmentation munitions. J. Def. Model. Simul. 17(1), 83–97 (2020)CrossRef
97.
O’Donohue, Commander Mark: Autonomous underwater vehicles. Niobe Papers 9(11) (2020)
98.
Keane, J., Joiner, K.: Experimental test and evaluation of autonomous underwater vehicles. Aust. J. Multi-Discip. Eng. 16(1), 67–79 (2020)CrossRef
99.
Nasu, H., Letts, D.: The legal characterization of lethal autonomous maritime systems: warship, torpedo, or naval mine? Int. Law Stud. 96(1), 4 (2020)
100.
Mvelle, G.: Fighting piracy in the gulf of guinea: small states’ pursuit of strategic autonomy. Revue internationale et strategique 2, 35–46 (2020)CrossRef
101.
Broohm, D.A., Wang, G., Gao, J.: Maritime security: a new strategy for merchant shipping to avoid piracy in the Gulf of Guinea. Open J. Soc. Sci. 8(5), 392–410 (2020)
102.
Grasso, R., Braca, P., Osler, J., Hansen, J.: Asset network planning: integration of environmental data and sensor performance for counter piracy. In: 21st European Signal Processing Conference (EUSIPCO 2013), pp. 1–5. IEEE (2013)
103.
Karahalios, H.: Appraisal of a ship’s cybersecurity efficiency: the case of piracy. J. Transp. Secur. 13, 1–23 (2020)CrossRef
104.
AU African Union, COIN Counterinsurgency, and CT Counterterrorism. Ctf counter terrorist financing ctf 150 combined task force 150 cwc chemical weapons convention dfg deutsche forschungsgemeinschaft/German research foundation
105.
Beccaro, A.: Isis in mosul and sirte: differences and similarities. Mediterr. Polit. 23(3), 410–417 (2018)CrossRef
106.
Bunker, R.J.: Keshavarz. Terrorist and insurgent teleoperated sniper rifles and machine guns, Alma (2016)
107.
Beccaro, A.: Isis in Libya and beyond, 2014–2016. J. N. Afr. Stud. 1–20 (2020)
108.
Gibbons-Neff, T.: Isis drones are attacking us troops and disrupting airstrikes in Raqqa, officials say. Washington Post 14 (2017)
109.
Hoenig, M.: Hezbollah and the use of drones as a weapon of terrorism. Public Interest Rep. 67(2) (2014)
110.
Stalinsky, S., Sosnow, R.: A decade of jihadi organizations’ use of drones—from early experiments by Hizbullah, Hamas, and Al-Qaeda to emerging national security crisis for the west as ISIS launches first attack drones. MEMRI-The Middle East Media Research Institute. February, 21 (2017)
111.
Shay, S.: The Houthi Maritime Threats in the Red Sea Basin, vol. 9. Institute for Policy and Strategy (2017)
112.
Rossiter, A.: Drone usage by militant groups: exploring variation in adoption. Def. Secur. Anal. 34(2), 113–126 (2018)MathSciNetCrossRef
113.
114.
Archambault, E., Veilleux-Lepage, Y.: Drone imagery in Islamic state propaganda: flying like a state. Int. Aff. 96(4), 955–973 (2020)CrossRef
115.
Naudé, W.: Artificial intelligence vs covid-19: limitations, constraints and pitfalls. Ai & Society, p. 1 (2020)
116.
Moon, M.J.: Fighting COVID-19 with agility, transparency, and participation: Wicked policy problems and new governance challenges. Public Adm. Rev. 80(4), 651–656 (2020)CrossRef
117.
Yakas, B.: Faa investigating” anti-covid-19 volunteer drone” filmed admonishing people in nyc (2020)
118.
Scott, J.E., Scott, C.H.: Models for drone delivery of medications and other healthcare items. In: Unmanned Aerial Vehicles: Breakthroughs in Research and Practice, pp. 376–392. IGI Global (2019)
119.
Ye, J.: The role of health technology and informatics in a global public health emergency: practices and implications from the covid-19 pandemic. JMIR Med. Inform. 8(7), e19866 (2020)CrossRef
120.
Abubakar, A.I., Omeke, K.G.,Öztürk, M., Hussain, S. and Imran, M.A.: The role of artificial intelligence driven 5G networks in COVID-19 outbreak: opportunities, challenges, and future outlook. Front. Comms. Net. (2020)
121.
Nair, V.V.: Drones as futuristic crime prevention strategy: situational review during covid-19 lockdown. J. Soc. Sci. 64(1–3), 22–29 (2020)
122.
Jat, D.S., Singh, C.: Artificial intelligence-enabled robotic drones for covid-19 outbreak. In: Intelligent Systems and Methods to Combat Covid-19, pp. 37–46. Springer (2020)
123.
Oguamanam, C.: Covid-19 and Africa: does one size fit all in public health intervention? Vulnerable: The Policy, Law and Ethics of COVID-19.University of Ottawa Press, Ottawa (2020) (Forthcoming in 2020)
124.
Vafea, M.T., Atalla, E., Georgakas, J., Shehadeh, F., Mylona, E.K., Kalligeros, M., Mylonakis, E.: Emerging technologies for use in the study, diagnosis, and treatment of patients with covid-19. Cell. Mol. Bioeng. 13(4), 249–257 (2020)CrossRef
125.
Zeng, Z., Chen, P.-J., Lew, A.A.: From high-touch to high-tech: Covid-19 drives robotics adoption. Tour. Geogr. 22, 1–11 (2020)CrossRef
126.
Bhaskar, S., Bradley, S., Sakhamuri, S., Moguilner, S., Chattu, V.K., Pandya, S., Schroeder, S., Ray, D., Banach, M.: Designing futuristic telemedicine using artificial intelligence and robotics in the covid-19 era. Front. Public Health 8, 708 (2020)CrossRef
127.
Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., Ruggiero, A.: Mitigating loneliness with companion robots in the covid-19 pandemic and beyond: an integrative framework and research agenda. J. Serv. Manag. (2020)
128.
Bhardwaj, A., Avasthi, V., Goundar, S.: Cyber security attacks on robotic platforms. Netw. Secur. 2019(10), 13–19 (2019)CrossRef
129.
Wang, C., Carzaniga, A., Evans, D., Wolf, A.: Security issues and requirements for internet-scale publish-subscribe systems. In: HICSS, p. 303. IEEE (2002)
130.
Esposito, C., Ciampi, M.: On security in publish/subscribe services: a survey. IEEE Commun. Surv. Tutor. 17(2), 966–997 (2015)CrossRef
131.
Dzung, D., Naedele, M., Von Hoff, T.P., Crevatin, M.: Security for industrial communication systems. Proc. IEEE 93(6), 1152–1177 (2005)CrossRef
132.
Laitinen, A., Niemelä, M., Pirhonen, J.: Demands of dignity in robotic care: recognizing vulnerability, agency, and subjectivity in robot-based, robot-assisted, and teleoperated elderly care. Tech. Res. Philos. Technol. 23(3), 366–401 (2019)CrossRef
133.
Choi, H., Kate, S., Aafer, Y., Zhang, X., Xu, D.: Cyber-physical inconsistency vulnerability identification for safety checks in robotic vehicles. In: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, pp. 263–278 (2020)
134.
Atamli, A.W., Martin, A.: Threat-based security analysis for the internet of things. In: 2014 International Workshop on Secure Internet of Things (SIoT), pp. 35–43. IEEE (2014)
135.
Hou, T., Wang, V.: Industrial espionage-a systematic literature review (slr). Comput. Secur. 98, 102019 (2020)
136.
Siman-Tov, D., Even, S.: A new level in the cyber war between Israel and Iran. INSS Insight (1328) (2020)
137.
Losa, L.: The impact of cyber capabilities on the Israeli–Iranian relationship (2020)
138.
Kaye, D.D., Efron, S.: Israel’s evolving Iran policy. Survival 62(4), 7–30 (2020)CrossRef
139.
Yousef, K.M.A., AlMajali, A., Ghalyon, S.A., Dweik, W., Mohd, B.J.: Analyzing cyber-physical threats on robotic platforms. Sensors 18(5), 1643 (2018)CrossRef
140.
Eun, Y.-S., Aßmann, J.S.: Cyberwar: taking stock of security and warfare in the digital age. Int. Stud. Perspect. 17(3), 343–360 (2016)
141.
Geerts, M.: Digitalization combined with organizational process innovation. The solution to the risk of industrial espionage? (2020)
142.
Klebanov, L.R., Polubinskaya, S.V.: Computer technologies for committing sabotage and terrorism. RUDN J. Law 24(3), 717–734 (2020)CrossRef
143.
144.
Sollins, K.R.: Iot big data security and privacy vs. innovation. IEEE Internet Things J. 6, 1–1 (2019)
145.
Noura, H.N., Hatoum, T., Salman, O., Yaacoub, J.-P., Chehab, A.: Lorawan security survey: issues, threats and possible mitigation techniques. Internet of Things, p. 100303 (2020)
146.
Salamai, A., Hussain, O.K., Saberi, M., Chang, E., Hussain, F.K.: Highlighting the importance of considering the impacts of both external and internal risk factors on operational parameters to improve supply chain risk management. IEEE Access 7, 49297–49315 (2019)CrossRef
147.
Priyadarshini, I.: Cyber security risks in robotics. In: Cyber Security and Threats: Concepts, Methodologies, Tools, and Applications, pp. 1235–1250. IGI Global (2018)
148.
Sobb, T., Turnbull, B., Moustafa, N.: Supply chain 4.0: a survey of cyber security challenges, solutions and future directions. Electronics 9(11), 1864 (2020)CrossRef
149.
Sha, K., Yang, T.A., Wei, W., Davari, S.: A survey of edge computing-based designs for IoT security. Digit. Commun. Netw. 6(2), 195–202 (2020)CrossRef
150.
Gaikwad, N.B., Ugale, H., Keskar, A., Shivaprakash, N.C.: The internet of battlefield things (IoBT) based enemy localization using soldiers location and gunshot direction. IEEE Internet of Things J. 7(12), 11725–11734 (2020)CrossRef
151.
Tehranipoor, M., Koushanfar, F.: A survey of hardware Trojan taxonomy and detection. IEEE Des. Test Comput. 27(1), 10–25 (2010)
152.
Wang, X., Mal-Sarkar, T., Krishna, A., Narasimhan, S., Bhunia, S.: Software exploitable hardware Trojans in embedded processor. In: 2012 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), pp. 55–58. IEEE (2012)
153.
Elmiligi, H., Gebali, F., El-Kharashi, M.W.: Multi-dimensional analysis of embedded systems security. Microprocess. Microsyst. 41, 29–36 (2016)CrossRef
154.
Clark, G.W., Doran, M.V., Andel, T.R.: Cybersecurity issues in robotics. In: 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), pp. 1–5. IEEE (2017)
155.
Falliere, N., Murchu, L.O., Chien, E.: W32. stuxnet dossier. White paper, Symantec Corp., Security Response 5(6), 29 (2011)
156.
Goyal, R., Sharma, S., Bevinakoppa, S., Watters, P.: Obfuscation of stuxnet and flame malware. Latest Trends Appl. Inform. Comput. 150, 154 (2012)
157.
Bencsáth, B., Pék, G., Buttyán, L., Felegyhazi, M.: The cousins of stuxnet: Duqu, flame, and gauss. Future Internet 4(4), 971–1003 (2012)CrossRef
158.
Kamiński, M.A.: Operation “olympic games.” Cyber-sabotage as a tool of American intelligence aimed at counteracting the development of Iran’s nuclear programme. Secur. Def. Q. 29(2), 63–71 (2020)CrossRef
159.
Horschig, D.: Cyber-weapons in nuclear counter-proliferation. Def. Secur. Anal. 36(3), 352–371 (2020)CrossRef
160.
Fruhlinger, J.: What is wannacry ransomware, how does it infect, and who was responsible (2017)
161.
Stallings, W.: Cryptography and Network Security: Principles and Practice. Pearson, Upper Saddle River (2017)
162.
Monikandan, S., Arockiam, L.: Confidentiality technique to enhance security of data in public cloud storage using data obfuscation. Indian J. Sci. Technol. 8(24), 1 (2015)CrossRef
163.
Bellovin, S.M., Merritt, M.: Encrypted key exchange: password-based protocols secure against dictionary attacks. In: 1992 IEEE Computer Society Symposium on Research in Security and Privacy, 1992. Proceedings, pp. 72–84. IEEE (1992)
164.
Irani, D., Balduzzi, M., Balzarotti, D., Kirda, E., Pu, C.: Reverse social engineering attacks in online social networks. In: International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, pp. 55–74. Springer (2011)
165.
Khan, M.H., Shah, M.A.: Survey on security threats of smartphones in internet of things. In: 2016 22nd International Conference on Automation and Computing (ICAC), pp. 560–566. IEEE (2016)
166.
Kc, G.S., Keromytis, A.D., Prevelakis, V.: Countering code-injection attacks with instruction-set randomization. In: Proceedings of the 10th ACM Conference on Computer and Communications Security, pp. 272–280. ACM (2003)
167.
Miller, J., Williams, A.B., Perouli, D.: A case study on the cybersecurity of social robots. In: Companion of the 2018 ACM/IEEE International Conference on Human–Robot Interaction, pp. 195–196. ACM (2018)
168.
Shahbaznezhad, H., Kolini, F., Rashidirad, M.: Employees’ behavior in phishing attacks: what individual, organizational, and technological factors matter? J. Comput. Inf. Syst. 1–12 (2020)
169.
Alabdan, R.: Phishing attacks survey: types, vectors, and technical approaches. Future Internet 12(10), 168 (2020)CrossRef
170.
Mo, Y., Garone, E., Casavola, A., Sinopoli, B.: False data injection attacks against state estimation in wireless sensor networks. In: 2010 49th IEEE Conference on Decision and Control (CDC), pp. 5967–5972. IEEE (2010)
171.
Senie, D., Ferguson, P.: Network ingress filtering: defeating denial of service attacks which employ IP source address spoofing. Network (1998)
172.
Gu, Q.: Packet-dropping attack. In: Encyclopedia of Cryptography and Security, pp. 899–902. Springer (2011)
173.
Navas, R.E., Le Bouder, H., Cuppens, N., Cuppens, F., Papadopoulos, G.Z.: Do not trust your neighbors! a small IoT platform illustrating a man-in-the-middle attack. In: International Conference on Ad-Hoc Networks and Wireless, pp. 120–125. Springer (2018)
174.
Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning (2017). arXiv preprint arXiv:​1712.​05526
175.
Alemzadeh, H., Chen, D., Li, X., Kesavadas, T., Kalbarczyk, Z.T., Iyer, R.K.: Targeted attacks on teleoperated surgical robots: dynamic model-based detection and mitigation. In: 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 395–406. IEEE (2016)
176.
Halderman, J.A., Schoen, S.D., Heninger, N., Clarkson, W., Paul, W., Calandrino, J.A., Feldman, A.J., Appelbaum, J., Felten, E.W.: Lest we remember: cold-boot attacks on encryption keys. Commun. ACM 52(5), 91–98 (2009)CrossRef
177.
Blackwell, T., Casner, D., Nelson, B., Wiley, S.: Self-balancing robot including an ultracapacitor power source, October 18 2011. US Patent 8,041,456
178.
Abomhara, M., Køien, G.M.: Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks. J. Cyber Secur. 4(1), 65–88 (2015)
179.
Rajendran, J., Kanuparthi, A.K., Zahran, M., Addepalli, S.K., Ormazabal, G., Karri, R.: Securing processors against insider attacks: a circuit-microarchitecture co-design approach. IEEE Des. Test 30(2), 35–44 (2013)CrossRef
180.
181.
Mansor, H., Markantonakis, K., Akram, R.N., Mayes, K.: Don’t brick your car: firmware confidentiality and rollback for vehicles. In: 2015 10th International Conference on Availability, Reliability and Security (ARES), pp. 139–148. IEEE (2015)
182.
Feily, M., Shahrestani, A., Ramadass, S.: A survey of botnet and botnet detection. In: Third International Conference on Emerging Security Information, Systems and Technologies, 2009. SECURWARE’09, pp. 268–273. IEEE (2009)
183.
Yih-Chun, H., Perrig, A., Johnson, D.B.: Wormhole attacks in wireless networks. IEEE J. Sel. Areas Commun. 24(2), 370–380 (2006)CrossRef
184.
Baccelli, E., Hahm, O., Gunes, M., Wahlisch, M., Schmidt, T.C.: Riot os: Towards an os for the internet of things. In: 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 79–80. IEEE (2013)
185.
Azar, C., Brostoff, G.: System and method for providing secure access to an electronic device using continuous facial biometrics, February 5 2013. US Patent 8,370,639
186.
Azar, C., Brostoff, G.: System and method for providing secure access to an electronic device using both a screen gesture and facial biometrics, January 7 2014. US Patent 8,627,096
187.
Tasevski, P.: Password Attacks and Generation Strategies. Tartu University, Faculty of Mathematics and Computer Sciences, Tartu (2011)
188.
189.
Neumann, P.G.: Denial-of-service attacks. Commun. ACM 43(4), 136–136 (2000)CrossRef
190.
191.
Amoozadeh, M., Raghuramu, A., Chuah, C.-N., Ghosal, D., Zhang, H.M., Rowe, J., Levitt, K.: Security vulnerabilities of connected vehicle streams and their impact on cooperative driving. IEEE Commun. Mag. 53(6), 126–132 (2015)CrossRef
192.
Chesaux, J.: Wireless access point spoofing and mobile devices geolocation using swarms of flying robots. Master optional semester project, Spring (2014)
193.
Kaufman, C.W., Pearlman, R.J., Gasser, M.: System for increasing the difficulty of password guessing attacks in a distributed authentication scheme employing authentication tokens, February 13 1996. US Patent 5,491,752
194.
Barcena, M.B., Wueest, C.: Insecurity in the internet of things. Security Response, Symantec (2015)
195.
Kumar, R., Pattnaik, P.K., Pandey, P.: Detecting and Mitigating Robotic Cyber Security Risks. IGI Global, Hershey (2017)CrossRef
196.
Schultz, E.E., Ray, E.: Rootkits: the ultimate malware threat. Inf. Secur. Manag. Handb. 2, 175 (2008)
197.
Denning, T., Matuszek, C., Koscher, K., Smith, J.R., Kohno, T.: A spotlight on security and privacy risks with future household robots: attacks and lessons. In: Proceedings of the 11th International Conference on Ubiquitous Computing, pp. 105–114. ACM (2009)
198.
Jiang, D., Omote, K.: An approach to detect remote access trojan in the early stage of communication. In: 2015 IEEE 29th International Conference on Advanced Information Networking and Applications (AINA), pp. 706–713. IEEE (2015)
199.
Maglaras, L.A., Jiang, J.: Intrusion detection in Scada systems using machine learning techniques. In: Science and Information Conference (SAI), 2014, pp. 626–631. IEEE (2014)
200.
Block, J.: A laws of war review of contemporary land-based missile defence system ‘iron dome’. Sci. Mil. S. Afr. J. Mil. Stud. 45(2), 105–128 (2017)
201.
Schneider, P., IFSH Hamburg: Recent trends in global maritime terrorism. Marit. Secur. Count. Terror. Lessons Marit. Piracy Narc. Interdiction 150, 187 (2020)
202.
Patterson, D.A., Bridgelall, R.: Attack risk modelling for the San Diego maritime facilities. Mar. Policy 104210 (2020)
203.
Morris, I.: War! what is it good for?: conflict and the progress of civilization from primates to robots. Farrar, Straus and Giroux (2014)
204.
Button, M.: Economic and industrial espionage (2020)
205.
Oruc, A., Sc MIET MIMarEST, M.: Claims of state-sponsored cyberattack in the maritime industry
206.
207.
Vermeulen, B., Pyka, A., Saviotti, P.P.: A taxonomic structural change perspective on the economic impact of robots and artificial intelligence on creative work. In: The Future of Creative Work. Edward Elgar Publishing (2020)
208.
Cooper, A.: How robots change the world; what automation really means for jobs and productivity. Technical report (Tech. Rep.). Oxford Economics, Oxford (2019)
209.
Acemoglu, D., Restrepo, P.: Robots and jobs: evidence from US labor markets. J. Polit. Econ. 128(6), 2188–2244 (2020)CrossRef
210.
Alemzadeh, H., Raman, J., Leveson, N., Kalbarczyk, Z., Iyer, R.K.: Adverse events in robotic surgery: a retrospective study of 14 years of FDA data. PloS one 11(4), e0151470 (2016)CrossRef
211.
Bloomfield, R.E.G.: Bullets to bytes: defending the United Kingdom in cyberspace (2019)
212.
Rotjan, R.D., Blum, J., Lewis, S.M.: Shell choice in pagurus longicarpus hermit crabs: does predation threat influence shell selection behavior? Behav. Ecol. Sociobiol. 56(2), 171–176 (2004)CrossRef
213.
Peterson, A.: Yes, terrorists could have hacked Dick Cheney’s heart. Washington Post (2013)
214.
Senthilkumar, K.S., Pirapaharan, K., Julai, N., Hoole, P.R.P., Othman, A.-H., Harikrishnan, R., Hoole, S.R.H.: Perceptron ANN control of array sensors and transmitters with different activation functions for 5g wireless systems. In: 2017 International Conference on Signal Processing and Communication (ICSPC), pp. 107–111. IEEE (2017)
215.
Checkoway, S., McCoy, D., Kantor, B., Anderson, D., Shacham, H., Savage, S., Koscher, K., Czeskis, A., Roesner, F., Kohno, T., et al.: Comprehensive experimental analyses of automotive attack surfaces. In: USENIX Security Symposium, pp. 77–92. San Francisco (2011)
216.
Turner, A., Glantz, K., Gall, J.: A practitioner-researcher partnership to develop and deliver operational value of threat, risk and vulnerability assessment training to meet the requirements of emergency responders. J. Homel. Secur. Emerg. Manag. 10(1), 319–332 (2013)
217.
Moalla, R., Labiod, H., Lonc, B., Simoni, N.: Risk analysis study of its communication architecture. In: 2012 Third International Conference on the Network of the Future (NOF), pp. 1–5. IEEE (2012)
218.
Alberts, C.J., Behrens, S.G., Pethia, R.D., Wilson, W.R.: Operationally critical threat, asset, and vulnerability evaluation (octave) framework, version 1.0. Technical report, Carnegie-Mellon Univ Pittsburgh PA Software Engineering Inst (1999)
219.
Zahra, B.F., Abdelhamid, B.: Risk analysis in internet of things using EBIOS. In: 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1–7. IEEE (2017)
220.
Méthode Harmonisée d’Analyse de Risques. Mehari. CLUSIF, France (2007)
221.
Barber, B., Davey, J.: The use of the CCTA risk analysis and management methodology Cramm in health information systems. Medinfo 92, 1589–1593 (1992)
222.
Secrétariat Général Défense Nationale. Ebios-expression des besoins et identification des objectifs de sécurité (2004)
223.
Süzen, A.A.: A risk-assessment of cyber attacks and defense strategies in industry 4.0 ecosystem. Int. J. Comput. Netw. Inf. Secur. 12(1) (2020)
224.
Brandstötter, M., Komenda, T., Ranz, F., Wedenig, P., Gattringer, H., Kaiser, L., Breitenhuber, G., Schlotzhauer, A., Müller, A., Hofbaur, M.: Versatile collaborative robot applications through safety-rated modification limits. In: International Conference on Robotics in Alpe-Adria Danube Region, pp. 438–446. Springer (2019)
225.
Komenda, T., Steiner, M., Rathmair, M., Brandstötter, M.: Introducing a morphological box for an extended risk assessment of human-robot work systems considering prospective system modifications. Gra, In: Joint Austrian Computer Vision and Robotics WorkshopAt (2019)
226.
Chemweno, P., Pintelon, L., Decre, W.: Orienting safety assurance with outcomes of hazard analysis and risk assessment: a review of the ISO 15066 standard for collaborative robot systems. Saf. Sci. 129, 104832 (2020)CrossRef
227.
Wan, N., Li, L., Ye, C., Wang, B.: Risk assessment in intelligent manufacturing process: a case study of an optical cable automatic arranging robot. IEEE Access 7, 105892–105901 (2019)CrossRef
228.
George, G., Thampi, S.M.: Vulnerability-based risk assessment and mitigation strategies for edge devices in the internet of things. Pervasive Mob. Comput. 59, 101068 (2019)CrossRef
229.
Huang, Y.-L., Sun, W.-L., Tang, Y.-H.: 3aram: a 3-layer AHP-based risk assessment model and its implementation for an industrial IoT cloud. In: 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 450–457. IEEE (2019)
230.
Radanliev, P., De Roure, D.C., Nurse, J.R.C., Montalvo, R.M., Cannady, S., Santos, O., Burnap, P., Maple, C., et al.: Future developments in standardisation of cyber risk in the internet of things (iot). SN Appl. Sci. 2(2), 169 (2020)CrossRef
231.
Lv, Z., Yang, H., Singh, A.K., Manogaran, G., Lv, H.: Trustworthiness in industrial IoT systems based on artificial intelligence. IEEE Trans. Ind. Inform. (2020)
232.
Afzaliseresht, N., Miao, Y., Michalska, S., Liu, Q., Wang, H.: From logs to stories: human-centred data mining for cyber threat intelligence. IEEE Access 8, 19089–19099 (2020)CrossRef
233.
Koloveas, P., Chantzios, T., Tryfonopoulos, C., Skiadopoulos, S.: A crawler architecture for harvesting the clear, social, and dark web for IoT-related cyber-threat intelligence. In: 2019 IEEE World Congress on Services (SERVICES), vol. 2642, pp. 3–8. IEEE (2019)
234.
Xu, Z., Parizi, R.M., Hammoudeh, M., Loyola-González, O.: Cyber Security Intelligence and Analytics: Proceedings of the 2020 International Conference on Cyber Security Intelligence and Analytics (CSIA 2020), vol. 2, 1147. Springer (2020)
235.
Gupta, S., Sabitha, A.S., Punhani, R.: Cyber security threat intelligence using data mining techniques and artificial intelligence. Int. J. Recent Technol. Eng. 8, 6133–6140 (2019)
236.
De Cubber, G., Doroftei, D., Rudin, K., Berns, K., Matos, A., Serrano, D., Sanchez, J., Govindaraj, S., Bedkowski, J., Roda, R., et al.: Introduction to the use of robotic tools for search and rescue (2017)
237.
Davahlia, A., Shamsib, M., Abaeic, G.: A lightweight anomaly detection model using SVM for WSNs in IoT through a hybrid feature selection algorithm based on GA and GWO. J. Comput. Secur. 7(1), 63–79 (2020)
238.
Pham, V., Seo, E., Chung, T.-M.: Lightweight convolutional neural network based intrusion detection system. J. Commun. 15(11) (2020)
239.
He, H., Gray, J., Cangelosi, A., Meng, Q., McGinnity, T.M., Mehnen, J.: The challenges and opportunities of artificial intelligence in implementing trustworthy robotics and autonomous systems. In: 3rd International Conference on Intelligent Robotic and Control Engineering (2020)
240.
Soe, Y.N., Feng, Y., Santosa, P.I., Hartanto, R., Sakurai, K.: Towards a lightweight detection system for cyber attacks in the IoT environment using corresponding features. Electronics 9(1), 144 (2020)CrossRef
241.
Sethumadhavan, S., Waksman, A., Suozzo, M., Huang, Y., Eum, J.: Trustworthy hardware from untrusted components. Commun. ACM 58(9), 60–71 (2015)CrossRef
242.
Huffmire, T., Brotherton, B., Wang, G., Sherwood, T., Kastner, R., Levin, T., Nguyen, T., Irvine, C.: Moats and drawbridges: an isolation primitive for reconfigurable hardware based systems. In: 2007 IEEE Symposium on Security and Privacy (SP), pp. 281–295. IEEE (2007)
243.
Waksman, A., Sethumadhavan, S.: Tamper evident microprocessors. In: 2010 IEEE Symposium on Security and Privacy (SP), pp. 173–188. IEEE (2010)
244.
Agrawal, D., Baktir, S., Karakoyunlu, D., Rohatgi, P., Sunar, B.: Trojan detection using ic fingerprinting. In: IEEE Symposium on Security and Privacy, 2007. SP’07, pp. 296–310. IEEE (2007)
245.
Pike, L., Hickey, P., Elliott, T., Mertens, E., Tomb, A.: Trackos: a security-aware real-time operating system. In: International Conference on Runtime Verification, pp. 302–317. Springer (2016)
246.
Abera, T., Asokan, N., Davi, L., Ekberg, J.-E., Nyman, T., Paverd, A., Sadeghi, A.-R., Tsudik, G.: C-flat: control-flow attestation for embedded systems software. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 743–754. ACM (2016)
247.
Wang, H., Zhang, C., Song, Y., Pang, B.: Robot arm perceptive exploration based significant slam in search and rescue environment. Int. J. Robot. Autom. 33(4) (2018)
248.
Romero, M., Frey, B., Southern, C., Abowd, G.D.: Brailletouch: designing a mobile eyes-free soft keyboard. In: Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, pp. 707–709. ACM (2011)
249.
Joint Task Force Transformation Initiative et al.: Guide for conducting risk assessments. Special Publication (NIST SP)-800-30 Rev 1 (2012)
250.
Kriaa, S., Pietre-Cambacedes, L., Bouissou, M., Halgand, Y.: A survey of approaches combining safety and security for industrial control systems. Reliab. Eng. Syst. Saf. 139, 156–178 (2015)CrossRef
251.
McLean, I., Szymanski, B., Bivens, A.: Methodology of risk assessment in mobile agent system design. In: Information Assurance Workshop, 2003. IEEE Systems, Man and Cybernetics Society, pp. 35–42. IEEE (2003)
252.
Guiochet, J., Martin-Guillerez, D., Powell, D.: Experience with model-based user-centered risk assessment for service robots. In: 2010 IEEE 12th International Symposium on High-Assurance Systems Engineering (HASE), pp. 104–113. IEEE (2010)
253.
Wagner, H.J., Alvarez, M., Kyjanek, O., Bhiri, Z., Buck, M., Menges, A.: Flexible and transportable robotic timber construction platform-tim. Autom. Constr. 120, 103400 (2020)CrossRef
254.
Diab, M., Pomarlan, M., Beßler, D., Akbari, A., Rosell, J., Bateman, J., Beetz, M.: Skillman-a skill-based robotic manipulation framework based on perception and reasoning. Robot. Auton. Syst. 134, 103653 (2020)CrossRef
255.
Choi, H., Kate, S., Aafer, Y., Zhang, X., Xu, D.: Software-based realtime recovery from sensor attacks on robotic vehicles. In: 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020), pp. 349–364 (2020)
256.
Beaudoin, L., Avanthey, L., Villard, C.: Porting ardupilot to esp32: towards a universal open-source architecture for agile and easily replicable multi-domains mapping robots. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 43, 933–939 (2020)CrossRef
257.
Huang, Y., Wang, W., Wang, Y., Jiang, T., Zhang, Q.: Lightweight sybil-resilient multi-robot networks by multipath manipulation. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 2185–2193. IEEE (2020)
258.
Wallhoff, F.: Fgnet-facial expression and emotion database. Technische Universität München (2004)
259.
Johnson, N.F., Jajodia, S.: Exploring steganography: seeing the unseen. Computer 31(2), 26–34 (1998)CrossRef
260.
Douglas, M., Bailey, K., Leeney, M., Curran, K.: An overview of steganography techniques applied to the protection of biometric data. Multimed. Tools Appl. 77(13), 17333–17373 (2018)CrossRef
261.
Woodward, J.D., Jr., Horn, C., Gatune, J., Thomas, A.: Biometrics: a look at facial recognition. Technical report, Rand Corp Santa Monica, CA (2003)
262.
Taupin, J.M.: Using forensic DNA evidence at trial: a case study approach. CRC Press, Boca Raton (2016)CrossRef
263.
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)CrossRef
264.
George, J.P.: Development of efficient biometric recognition algorithms based on fingerprint and face. Ph.D. thesis, Christ University (2012)
265.
Al-Ani, M.S., Rajab, M.A.: Biometrics hand geometry using discrete cosine transform (DCT). Sci. Technol. 3(4), 112–117 (2013)
266.
Jain, A.K., Kumar, A.: Biometric recognition: an overview. In: Second Generation Biometrics: The Ethical, Legal and Social Context, pp. 49–79. Springer (2012)
267.
Wei, X., Wang, T., Tang, C., Fan, J.: Collaborative mobile jammer tracking in multi-hop wireless network. Future Gener. Comput. Syst. 78, 1027–1039 (2018)CrossRef
268.
Nguyen, M.-H.: The relationship between password-authenticated key exchange and other cryptographic primitives. In: Theory of Cryptography Conference, pp. 457–475. Springer (2005)
269.
270.
Song, R.: Advanced smart card based password authentication protocol. Comput. Stand. Interfaces 32(5–6), 321–325 (2010)CrossRef
271.
Chaudhry, S.A., Farash, M.S., Naqvi, H., Sher, M.: A secure and efficient authenticated encryption for electronic payment systems using elliptic curve cryptography. Electron. Commer. Res. 16(1), 113–139 (2016)CrossRef
272.
He, D., Gao, Y., Chan, S., Chen, C., Jiajun, B.: An enhanced two-factor user authentication scheme in wireless sensor networks. Ad Hoc Sens. Wirel. Netw. 10(4), 361–371 (2010)
273.
Yeh, H.-L., Chen, T.-H., Liu, P.-C., Kim, T.-H., Wei, H.-W.: A secured authentication protocol for wireless sensor networks using elliptic curves cryptography. Sensors 11(5), 4767–4779 (2011)CrossRef
274.
Chen, T.-H., Shih, W.-K.: A robust mutual authentication protocol for wireless sensor networks. ETRI J. 32(5), 704–712 (2010)CrossRef
275.
Kim, J., Lee, D., Jeon, W., Lee, Y., Won, D.: Security analysis and improvements of two-factor mutual authentication with key agreement in wireless sensor networks. Sensors 14(4), 6443–6462 (2014)CrossRef
276.
Xue, K., Ma, C., Hong, P., Ding, R.: A temporal-credential-based mutual authentication and key agreement scheme for wireless sensor networks. J. Netw. Comput. Appl. 36(1), 316–323 (2013)CrossRef
277.
Wang, D., Li, W., Wang, P.: Measuring two-factor authentication schemes for real-time data access in industrial wireless sensor networks. IEEE Trans. Ind. Inform. (2018)
278.
Li, C.-T., Weng, C.-Y., Lee, C.-C.: An advanced temporal credential-based security scheme with mutual authentication and key agreement for wireless sensor networks. Sensors 13(8), 9589–9603 (2013)CrossRef
279.
Gope, P., Hwang, T., et al.: A realistic lightweight anonymous authentication protocol for securing real-time application data access in wireless sensor networks. IEEE Trans. Ind. Electron. 63(11), 7124–7132 (2016)CrossRef
280.
Jiang, Q., Ma, J., Xiang, L., Tian, Y.: An efficient two-factor user authentication scheme with unlinkability for wireless sensor networks. Peer-to-peer Netw. Appl. 8(6), 1070–1081 (2015)CrossRef
281.
Fan, W., Lili, X., Kumari, S., Li, X.: A new and secure authentication scheme for wireless sensor networks with formal proof. Peer-to-Peer Netw. Appl. 10(1), 16–30 (2017)CrossRef
282.
Amin, R., Biswas, G.P.: A secure light weight scheme for user authentication and key agreement in multi-gateway based wireless sensor networks. Ad Hoc Netw. 36, 58–80 (2016)CrossRef
283.
Srinivas, J., Mukhopadhyay, S., Mishra, D.: Secure and efficient user authentication scheme for multi-gateway wireless sensor networks. Ad Hoc Netw. 54, 147–169 (2017)CrossRef
284.
González Muñiz, M., Laud, P.: On the (im) possibility of perennial message recognition protocols without public-key cryptography. In: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 1510–1515. ACM (2011)
285.
Kumar, P., Choudhury, A.J., Sain, M., Lee, S.-G., Lee, H.-J.: Ruasn: a robust user authentication framework for wireless sensor networks. Sensors 11(5), 5020–5046 (2011)CrossRef
286.
Eisenbarth, T., Kumar, S., Paar, C., Poschmann, A., Uhsadel, L.: A survey of lightweight-cryptography implementations. IEEE Des. Test Comput. 6, 522–533 (2007)CrossRef
287.
De Canniere, C., Dunkelman, O., Knežević, M.: Katan and ktantan—a family of small and efficient hardware-oriented block ciphers. In: Cryptographic Hardware and Embedded Systems-CHES 2009, pp. 272–288. Springer (2009)
288.
Gong, Z., Nikova, S., Law, Y.W.: Klein: a new family of lightweight block ciphers. In: International Workshop on Radio Frequency Identification: Security and Privacy Issues, pp. 1–18. Springer (2011)
289.
Lim, C.H., Korkishko, T.: mcrypton—a lightweight block cipher for security of low-cost rfid tags and sensors. In: International Workshop on Information Security Applications, pp. 243–258. Springer (2005)
290.
Shibutani, K., Isobe, T., Hiwatari, H., Mitsuda, A., Akishita, T., Shirai, T.: Piccolo: an ultra-lightweight blockcipher. In: International Workshop on Cryptographic Hardware and Embedded Systems, pp. 342–357. Springer (2011)
291.
Bogdanov, A., Knudsen, L.R., Leander, G., Paar, C., Poschmann, A., Robshaw, M.J.B., Seurin, Y., Vikkelsoe, C.: Present: an ultra-lightweight block cipher. In: International Workshop on Cryptographic Hardware and Embedded Systems, pp. 450–466. Springer (2007)
292.
Suzaki, T., Minematsu, K., Morioka, S., Kobayashi, E.: A lightweight block cipher for multiple platforms. In: International Conference on Selected Areas in Cryptography, pp. 339–354. Springer (2012)
293.
Yap, H., Khoo, K., Poschmann, A., Henricksen, M.: Epcbc-a block cipher suitable for electronic product code encryption. In: International Conference on Cryptology and Network Security, pp. 76–97. Springer (2011)
294.
Dworkin, M.: Recommendation for block cipher modes of operation. Methods and techniques. Technical report, National Inst of Standards and Technology, Gaithersburg, MD, Computer Security Div (2001)
295.
Breiling, B., Dieber, B., Schartner, P.: Secure communication for the robot operating system. In: 2017 Annual IEEE International Systems Conference (SysCon), pp. 1–6. IEEE (2017)
296.
Hussein, A., Elhajj, I.H., Chehab, A., Kayssi, A.: Securing diameter: comparing tls, dtls, and ipsec. In: 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), pp. 1–8. IEEE (2016)
297.
Dieber, B., Kacianka, S., Rass, S., Schartner, P.: Application-level security for ros-based applications. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4477–4482. IEEE (2016)
298.
Hussaini, S.: Cyber security in cloud using blowfish encryption. Int. J. Inf. Technol. (IJIT), 6(5) (2020)
299.
Tian, N.: Cloud-edge hybrid robotic systems for physical human robot interactions. Ph.D. thesis, UC Berkeley (2020)
300.
Chavhan, S., Doriya, R.: Secured map building using elliptic curve integrated encryption scheme and kerberos for cloud-based robots. In: 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 157–164. IEEE (2020)
301.
Strobel, V., Ferrer, E.C., Dorigo, M.: Blockchain technology secures robot swarms: a comparison of consensus protocols and their resilience to byzantine robots. Front. Robot. AI 7, 54 (2020)CrossRef
302.
Alcaraz, C., Rubio, J.E., Lopez, J.: Blockchain-assisted access for federated smart grid domains: coupling and features. J. Parallel Distrib. Comput. (2020)
303.
Fagiolini, A., Pellinacci, M., Valenti, G., Dini, G., Bicchi, A.: Consensus-based distributed intrusion detection for multi-robot systems. In: IEEE International Conference on Robotics and Automation, 2008. ICRA 2008, pp. 120–127. IEEE (2008)
304.
Reategui, E.B., Campbell, J.: A classification system for credit card transactions. In: European Workshop on Advances in Case-Based Reasoning, pp. 280–291. Springer (1994)
305.
Bonifacio, J.M., Cansian, A.M., De Carvalho, A.C.P.L.F., Moreira, E.S.: Neural networks applied in intrusion detection systems. In: The 1998 IEEE International Joint Conference on Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence, vol. 1, pp. 205–210. IEEE (1998)
306.
Yeung, D.-Y., Chow, C.: Parzen-window network intrusion detectors. In: Object Recognition Supported by User Interaction for Service Robots, vol. 4, pp. 385–388. IEEE (2002)
307.
Vigna, G., Robertson, W., Kher, V., Kemmerer, R.A.: A stateful intrusion detection system for world-wide web servers. In: Null, p. 34. IEEE (2003)
308.
Onat, I., Miri, A.: An intrusion detection system for wireless sensor networks. In: IEEE International Conference on Wireless and Mobile Computing, Networking And Communications, 2005.(WiMob’2005), vol. 3, pp. 253–259. IEEE (2005)
309.
Gudadhe, M., Prasad, P., Wankhade, L.K.: A new data mining based network intrusion detection model. In: 2010 International Conference on Computer and Communication Technology (ICCCT), pp. 731–735. IEEE (2010)
310.
Om, H., Kundu, A.: A hybrid system for reducing the false alarm rate of anomaly intrusion detection system. In: 2012 1st International Conference on Recent Advances in Information Technology (RAIT), pp. 131–136. IEEE (2012)
311.
Rath, M., Pattanayak, B.K.: Security protocol with ids framework using mobile agent in robotic manet. Int. J. Inf. Secur. Privacy (IJISP) 13(1), 46–58 (2019)CrossRef
312.
Rivera, S., Iannillo, A.K., et al.: Ros-immunity: integrated approach for the security of ros-enabled robotic systems (2020)
313.
Zhou, Y., Mazzuchi, T.A., Sarkani, S.: M-adaboost-a based ensemble system for network intrusion detection. Expert Syst. Appl. 162 (2020)
314.
Gorbenko, A., Popov, V.: Abnormal behavioral pattern detection in closed-loop robotic systems for zero-day deceptive threats. In: 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 1–6. IEEE (2020)
315.
Almalawi, A., Fahad, A., Tari, Z., Khan, A.I., Alzahrani, N., Bakhsh, S.T., Alassafi, M.O., Alshdadi, A., Qaiyum, S.: Add-on anomaly threshold technique for improving unsupervised intrusion detection on scada data. Electronics 9(6), 1017 (2020)CrossRef
316.
Spitzner, L.: Honeypots: Tracking Hackers, vol. 1. Addison-Wesley, Reading (2003)
317.
Zhang, F., Zhou, S., Qin, Z., Liu, J.: Honeypot: a supplemented active defense system for network security. In: Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies, 2003. PDCAT’2003, pp. 231–235. IEEE (2003)
318.
Irvene, C., Formby, D., Litchfield, S., Beyah, R.: Honeybot: a honeypot for robotic systems. Proc. IEEE 106(1), 61–70 (2018)CrossRef
319.
320.
Spitzner, L.: Specter: a commercial honeypot solution for windows. Acesso em 26(08) (2003)
321.
Provos, N.: Honeyd-a virtual honeypot daemon. In: 10th DFN-CERT Workshop, Hamburg, Germany, vol. 2, p. 4 (2003)
322.
La, Q.D., Quek, T.Q.S., Lee, J.: A game theoretic model for enabling honeypots in IoT networks. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2016)
323.
Spitzner, L.: The honeynet project: trapping the hackers. IEEE Secur. Privacy 99(2), 15–23 (2003)CrossRef
324.
Terra, A., Riaz, H., Raizer, K., Hata, A., Inam, R.: Safety vs. efficiency: Ai-based risk mitigation in collaborative robotics. In: 2020 6th International Conference on Control, Automation and Robotics (ICCAR), pp. 151–160. IEEE (2020)
325.
Wang, C., Tok, Y.C., Poolat, R., Chattopadhyay, S., Elara, M.R.: How to secure autonomous mobile robots? an approach with fuzzing, detection and mitigation. J. Syst. Archit. 101838 (2020)
326.
Bykovsky, A.Y.: Heterogeneous network architecture for integration of AI and quantum optics by means of multiple-valued logic. Quantum Rep. 2(1), 126–165 (2020)CrossRef
327.
Alamer, A.: A secure anonymous tracing fog-assisted method for the internet of robotic things. Library Hi Tech (2020)
328.
Szalachowski, P., Ksiezopolski, B., Kotulski, Z.: Cmac, ccm and gcm/gmac: advanced modes of operation of symmetric block ciphers in wireless sensor networks. Inf. Process. Lett. 110(7), 247–251 (2010)MathSciNetMATHCrossRef
329.
Abeykoon, I., Feng, X.: A forensic investigation of the robot operating system. In: 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 851–857. IEEE (2017)
330.
Erbacher, R.F., Christiansen, K., Sundberg, A., et al.: Visual network forensic techniques and processes. In: 1st Annual Symposium on Information Assurance: Intrusion Detection and Prevention, p. 72 (2006)
331.
Noura, H.N., Melki, R., Chehab, A., Fernandez, J.H.: Efficient and robust data availability solution for hybrid plc/rf systems. Comput. Netw. 185, 107675 (2021)CrossRef
332.
Chigan, C., Li, L., Ye, Y.: Resource-aware self-adaptive security provisioning in mobile ad hoc networks. In: 2005 IEEE Wireless Communications and Networking Conference, vol. 4, pp. 2118–2124. IEEE (2005)
333.
Bethencourt, J., Sahai, A., Waters, B.: Ciphertext-policy attribute-based encryption. In: IEEE Symposium on Security and Privacy, 2007. SP’07, pp. 321–334. IEEE (2007)
334.
Needham, R.M., Wheeler, D.J.: Tea extensions. Report (Cambridge University, Cambridge, UK, 1997) Google Scholar (1997)
335.
Hu, W., Corke, P., Shih, W.C., Overs, L.: secfleck: a public key technology platform for wireless sensor networks. In: European Conference on Wireless Sensor Networks, pp. 296–311. Springer (2009)
336.
Hu, W., Tan, H., Corke, P., Shih, W.C., Jha, S.: Toward trusted wireless sensor networks. ACM Trans. Sens. Netw. (TOSN) 7(1), 5 (2010)
337.
Touati, L., Challal, Y., Bouabdallah, A.: C-cp-abe: cooperative ciphertext policy attribute-based encryption for the internet of things. In: 2014 International Conference on Advanced Networking Distributed Systems and Applications (INDS), pp. 64–69. IEEE (2014)
338.
Touati, L., Challal, Y.: Collaborative kp-abe for cloud-based internet of things applications. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–7. IEEE (2016)
339.
Goyal, V., Pandey, O., Sahai, A., Waters, B.: Attribute-based encryption for fine-grained access control of encrypted data. In: Proceedings of the 13th ACM Conference on Computer and communications security, pp. 89–98. ACM (2006)
340.
Hohenberger, S., Lysyanskaya, A.: How to securely outsource cryptographic computations. In: Theory of Cryptography Conference, pp. 264–282. Springer (2005)
341.
342.
Laih, C.-S., Kuo, W.-C.: New signature schemes based on factoring and discrete logarithms. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 80(1), 46–53 (1997)
343.
Courtois, N.T., Finiasz, M., Sendrier, N.: How to achieve a Mceliece-based digital signature scheme. In: International Conference on the Theory and Application of Cryptology and Information Security, pp. 157–174. Springer (2001)
344.
345.
Hoffstein, J., Pipher, J., Silverman, J.H.: Ntru: a ring-based public key cryptosystem. In: International Algorithmic Number Theory Symposium, pp. 267–288. Springer (1998)
346.
Noura, H.N., Melki, R., Chehab, A.: Efficient data confidentiality scheme for 5g wireless NOMA communications. J. Inf. Secur. Appl. 58 (2021)
347.
Noura, H.N., Melki, R., Kanj, R., Chehab, A.: Secure MIMO d2d communication based on a lightweight and robust PLS cipher scheme. Wirel. Netw. 27(1), 557–574 (2021)CrossRef
348.
Trappe, W., Howard, R., Moore, R.S.: Low-energy security: limits and opportunities in the internet of things. IEEE Secur. Privacy 13(1), 14–21 (2015)CrossRef
349.
Mukherjee, A.: Physical-layer security in the internet of things: sensing and communication confidentiality under resource constraints. Proc. IEEE 103(10), 1747–1761 (2015)CrossRef
350.
Noura, H.N., Melki, R., Chehab, A., Mansour, M.M., Martin, S.: Efficient and secure physical encryption scheme for low-power wireless m2m devices. In: 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 1267–1272. IEEE (2018)
351.
Melki, R., Noura, H.N., Mansour, M.M., Chehab, A.: An efficient OFDM-based encryption scheme using a dynamic key approach. IEEE Internet of Things J. 6(1), 361–378 (2018)CrossRef
352.
Noura, H.N., Melki, R., Chehab, A., Hernandez Fernandez, J.: Efficient and secure message authentication algorithm at the physical layer. Wirel. Netw. 1–15 (2020)
353.
Bellare, M., Canetti, R., Krawczyk, H.: Keying hash functions for message authentication. In: Annual International Cryptology Conference, pp. 1–15. Springer (1996)
354.
Noura, H.N., Salman, O., Chehab, A., Couturier, R.: Distlog: a distributed logging scheme for IoT forensics. Ad Hoc Netw. 98, 102061 (2020)CrossRef
355.
Melki, R., Noura, H.N., Chehab, A.: Lightweight multi-factor mutual authentication protocol for IoT devices. Int. J. Inf. Secur. 19, 1–16 (2019)
356.
Noura, H.N., Melki, R., Chehab, A.: Secure and lightweight mutual multi-factor authentication for IoT communication systems. In: 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), pp. 1–7. IEEE (2019)
357.
Noura, H.N., Salman, O., Couturier, R., Chehab, A.: Novel one round message authentication scheme for constrained IoT devices. J. Ambient Intell. Hum. Comput. 1–17 (2021)
358.
Noura, H.N., Noura, M., Salman, O., Couturier, R., Chehab, A.: Efficient & secure image availability and content protection. Multimed. Tools Appl. 79, 22869–22904 (2020)CrossRef
359.
Noura, H.N., Chehab, A., Sleem, L., Noura, M., Couturier, R., Mansour, M.M.: One round cipher algorithm for multimedia IoT devices. Multimed. Tools Appl. 77, 1–31 (2018)CrossRef
360.
Noura, H., Chehab, A., Couturier, R.: Lightweight dynamic key-dependent and flexible cipher scheme for IoT devices. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–8. IEEE (2019)
361.
Noura, H.N., Couturier, R., Pham, C., Chehab, A.: Lightweight stream cipher scheme for resource-constrained IoT devices. In: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 1–8. IEEE (2019)
362.
Noura, H.N., Chehab, A., Couturier, R.: Overview of efficient symmetric cryptography: dynamic vs static approaches. In: 2020 8th International Symposium on Digital Forensics and Security (ISDFS), pp. 1–6. IEEE (2020)
363.
Noura, H.N., Melki, R., Malli, M., Chehab, A.: Lightweight and secure cipher scheme for multi-homed systems. Wirel. Netw. 1–18
364.
Noura, H.N., Salman, O., Chehab, A., Couturier, R.: Preserving data security in distributed fog computing. Ad Hoc Netw. 94, 101937 (2019)CrossRef
365.
Noura, H.N., Salman, O., Kaaniche, N., Sklavos, N., Chehab, A., Couturier, R.: Tresc: Towards redesigning existing symmetric ciphers. Microprocess. Microsyst. 103478 (2020)
366.
Fawaz, Z., Noura, H.N., Mostefaoui, A.: Securing jpeg-2000 images in constrained environments: a dynamic approach. Multimed. Syst. 24(6), 669–694 (2018)CrossRef
367.
Mostefaoui, A., Noura, H.N., Fawaz, Z.: An integrated multimedia data reduction and content confidentiality approach for limited networked devices. Ad Hoc Netw. 32, 81–97 (2015)CrossRef
368.
Salman, O., Elhajj, I.H., Chehab, A., Kayssi, A.: A multi-level internet traffic classifier using deep learning. In: 2018 9th International Conference on the Network of the Future (NOF), pp. 68–75 (2018)
369.
Salman, O., Chaddad, L., Elhajj, I.H., Chehab, A., Kayssi, A.: Pushing intelligence to the network edge. In: 2018 Fifth International Conference on Software Defined Systems (SDS), pp. 87–92 (2018)
Metadata
Title
Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations
Authors
Jean-Paul A. Yaacoub
Hassan N. Noura
Ola Salman
Ali Chehab
Publication date
19-03-2021
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Information Security / Issue 1/2022
Print ISSN: 1615-5262
Electronic ISSN: 1615-5270
DOI
https://doi.org/10.1007/s10207-021-00545-8

Other articles of this Issue 1/2022

International Journal of Information Security 1/2022 Go to the issue

regular contribution

DAPP: automatic detection and analysis of prototype pollution vulnerability in Node.js modules

regular contribution

[m]allotROPism: a metamorphic engine for malicious software variation development

regular contribution

A new smart smudge attack using CNN

regular contribution

Privacy preserving data sharing and analysis for edge-based architectures

regular contribution

Public key versus symmetric key cryptography in client–server authentication protocols

regular contribution

Automatic analysis of attack graphs for risk mitigation and prioritization on large-scale and complex networks in Industry 4.0

Premium Partner

    Image Credits