nach oben

Neural Computing and Applications

Erschienen in:

07.08.2018 | S.I. : Machine Learning Applications for Self-Organized Wireless Networks

Privacy and security of big data in cyber physical systems using Weibull distribution-based intrusion detection

verfasst von: R. Gifty, R. Bharathi, P. Krishnakumar

Erschienen in: Neural Computing and Applications | Sonderheft 1/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The volume of data collected from cyber physical systems (CPS) is huge, and we use big data techniques to manage and store the data. Big data in CPS is concerned with the massive heterogeneous data streams, which are acquired from autonomous sources and computed in distributed data storage system. In order to handle the size, complexity and rate of availability of data, it requires new techniques that can inspect and interpret useful knowledge from large streams of information, which impose challenges on the design and management of CPS in multiple aspects such as performance, energy efficiency, security, privacy, reliability, sustainability, fault tolerance, scalability and flexibility. This paper focuses on the security and privacy aspects in managing big data for CPS and reviews recent challenges in data privacy. We also present a protection framework for intrusion detection and analyze the performance parameters, reliability and failure rate in a malicious big data context.

Vorheriger Artikel RETRACTED ARTICLE: An efficient technique for mitigating stealthy attacks using MNDA in MANET

Nächster Artikel A multi-objective location and channel model for ULS network

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Giraldo J (2017) “Security and privacy in cyber-physical systems: a survey of surveys. IEEE Des Test 34(4):7–17. https://doi.org/10.1109/mdat.2017.2709310 CrossRef

Howser G (2017) Using information-flow methods to analyze the security of cyber-physical systems. IEEE Comput 40(4):17–26. https://doi.org/10.1109/mc.2017.112 CrossRef

Jara AJ (2014) Big data for cyber physical systems—an analysis of challenges, solutions and opportunities. In: IEEE 8th international conference on innovative mobile and Internet services in ubiquitous computing, 2–4 July 2014, pp 376–380. https://doi.org/10.1109/imis.2014.139

http://indiatoday.intoday.in/story/petya-ransomware-major-global-cyber-attack-wannacry-jawaharlal-nehru-port-trust/1/988915.html

http://www.telegraph.co.uk/technology/2016/12/20/hackers-could-take-control-plane-using-in-flight-entertainment/

He H (2016) The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence. In: IEEE Congress on Evolutionary Computation, pp 1015–1021. https://doi.org/10.1109/cec.2016.7743900

Puttonen J (2015) Security in cloud-based cyber-physical systems. In: IEEE 10th international conference on P2P, parallel, grid, cloud and Internet computing, pp 671–676. https://doi.org/10.1109/3pgcic.2015.30

Terzi DS (2015) A survey on security and privacy issues of big data. In: IEEE 10th international conference for Internet technology and secured transactions (ICITST), 14–16 December 2015. https://doi.org/10.1109/icitst.2015.7412089

Pasqualetti F (2015) Design and operation of secure cyber-physical systems. IEEE Embed Syst Lett 7(1):3–6. https://doi.org/10.1109/les.2014.2367100 MathSciNetCrossRef

10.

Pasqualetti F (2013) Attack detection and identification in cyber physical systems. IEEE Tran Autom Control 58(11):2715–2729. https://doi.org/10.1109/tac.2013.2266831 MathSciNetCrossRefMATH

11.

Wang EK (2010) Security issues and challenges for cyber physical system. In: IEEE/ACM international conference on cyber, physical and social computing, pp 733–738. https://doi.org/10.1109/greencom-cpscom

12.

Humayed A (2017) Cyber physical systems security—a survey. IEEE Internet Things 1(99):1. https://doi.org/10.1109/jiot.2017.2703172

13.

Eastman R (215) Big data and predictive analytics: on the cybersecurity front line. IDC Whitepaper, February 2015

14.

Cheng P (2016) Cyber security for industrial control systems: from the viewpoint of close-loop, pp 133–145

15.

AlTawy R (2016) Security trade-offs in cyber physical systems: a case study survey on implantable medical devices. IEEE Access 4:959–979. https://doi.org/10.1109/access.2016.2521727 CrossRef

16.

Kocabas O (2016) Emerging security mechanisms for medical cyber physical systems. IEEE Trans Comput Biol Bioinform 13(3):401–416. https://doi.org/10.1109/tcbb.2016.2520933 CrossRef

17.

Elias (2016) A brief survey of security approaches for cyber-physical systems. In: IEEE 8th IFIP international conference on new technologies, mobility and security, pp 1–5. https://doi.org/10.1109/ntms.2016.7792424

18.

Lu R, Liang X, Li X, Lin X, Shen X (2012) EPPA: an efficient and privacy preserving aggregation scheme for secure smart grid communications. IEEE Trans Parallel Distrib Syst 23(9):1621–1631CrossRef

19.

Siddharth S (2012) Cyber-physical system security for the electric power grid. Proc IEEE 100(1):210–224CrossRef

20.

Taylor C, Alves-Foss J (2001) NATE: network analysis of anomalous traffic events, a low-cost approach. In: Proceedings of workshop on new security paradigms, Cloudcroft, NM, pp 89–96

21.

Evers K (2017) Security measurement on a cloud-based cyber-physical system used for intelligent transportation. In: IEEE international conference on vehicular electronics and safety, 27–28 June 2017, pp 97–102. https://doi.org/10.1109/icves.2017.7991908

22.

Mitchell R, Chen R (2014) Adaptive intrusion detection of malicious unmanned air vehicles using behavior rule specifications. IEEE Trans Syst Man Cybern Syst 44(5):593–604CrossRef

23.

Kumar SAP (2017) Vulnerability assessment for security in aviation cyber-physical systems. In: IEEE 4th international conference on cyber security and cloud computing, 26–28 June 2017. https://doi.org/10.1109/cscloud.2017.17

24.

Cho C-S (2016) Cyberphysical security and dependability analysis of digital control systems in nuclear power plants. IEEE Trans Syst Man Cybern Syst 46(3):356–369. https://doi.org/10.1109/tsmc.2015.2452897 CrossRef

25.

Shafi Q (2012) Cyber physical systems security: a brief survey. In: IEEE 12th international conference on computational science and its applications, pp 146–150. https://doi.org/10.1109/iccsa.2012.36

26.

Louthan G, Hardwicke P, Hawrylak P, Hale J (2011) Toward hybrid attack dependency graphs. In: The seventh annual workshop on cyber security and information intelligence research, CSIIRW’11, pp 62:1–62:1, Oak Ridge, TN, USA, October 2011

27.

Li F, Clarke N, Papadaki M, Dowland P (2010) Behaviour profiling on mobile devices. In: International conference on emerging security technologies, pp 77–82, Canterbury, UK, September 2010

28.

Kirkpatrick M, Ghinita G, Bertino E (2012) Resilient authenticated execution of critical applications in untrusted environments. IEEE Trans Dependable Secure Comput 9(4):597–609CrossRef

29.

Gao Y (2013) Analysis of security threats and vulnerability for cyber-physical systems. In: IEEE Proceedings of 2013 3rd international conference on computer science and network theory, 12–13 Oct 2013. https://doi.org/10.1109/iccsnt.2013.6967062

30.

Mitchell R (2013) Effect of intrusion detection and response on reliability of cyber physical systems. IEEE Trans Reliab 62(1):199–210. https://doi.org/10.1109/tr.2013.2240891 CrossRef

31.

Jain P (2016) Big data privacy: a technological perspective and review. J Big Data. https://doi.org/10.1186/s40537-016-0059-y

32.

Louvieris P, Clewley N, Liu X (2013) Effects-based feature identification fornetwork intrusion detection. Neurocomputing 121:265–273CrossRef

33.

Mitchell R, Chen IR (2013) Survey of intrusion detection in wireless network applications. Elsevier Computer Networks

34.

Ma Y, Cao H, Ma J (2008) The intrusion detection method based on game theory in wireless sensor network. In: First IEEE international conference on Ubi-Media computing, pp 326–331, Lanzhou University, China, August 2008

35.

Wang K, Stolfo S (2004) Anomalous payload-based network intrusion detection. In: Recent advances in intrusion detection, Sophia Antipolis, pp 203–222

36.

Mitchell R, Chen IR, Eltoweissy M (2010) Signalprint-based intrusion detection in wireless networks. In: Security in emerging wireless communication and networking systems, pp 77–88, Athens, Greece, September 2010

37.

Cabrera JBD (2004) On the statistical distribution of processing times in network intrusion detection. In: 43rd IEEE conference on decision and control. https://doi.org/10.1109/cdc.2004.1428609

38.

Sivasamy AA (2015) A dynamic intrusion detection system based on multivariate hotelling’s T² statistics approach for network environments. The Sci World J, vol 2015, Article ID 850153, 9 pp. https://doi.org/10.1155/2015/850153

Titel: Privacy and security of big data in cyber physical systems using Weibull distribution-based intrusion detection
verfasst von: R. Gifty
R. Bharathi
P. Krishnakumar
Publikationsdatum: 07.08.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3635-6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Sonderheft 1/2019

TRec: an efficient recommendation system for hunting passengers with deep neural networks

Earliest deadline first scheduling technique for different networks in network control system

An efficient top-k ranking method for service selection based on ε-ADMOPSO algorithm

Special issue on machine learning applications for self-organized wireless sensor networks

Research on exchange rate forecasting based on deep belief network

Jointly network: a network based on CNN and RBM for gesture recognition