Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Secure Big Data Transmission with Trust Management for the Internet of Things (IoT) Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Deep Learning Meets Malware Detection: An Investigation

verfasst von : Biozid Bostami, Mohiuddin Ahmed

Erschienen in: Combating Security Challenges in the Age of Big Data

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

From the dawn of computer programs, malware programs were originated and still with us. With evolving of technology, malware programs are also evolving. It is considered as one of the prime issues regarding cyber world security. Damage caused by the malware programs ranges from system failure to financial loss. Traditional approach for malware classification approach are not very suitable for advance malware programs. For the continuously evolving malware ecosystem deep learning approaches are more suitable as they are faster and can predict malware more effectively. To our best of knowledge, there has not substantial research done on deep learning based malware detection on different sectors like: IoT, Bio-medical sectors and Cloud platforms. The key contribution of this chapter will be creating directions of malware detection depending on deep learning. The chapter will be beneficial for graduate level students, academicians and researchers in this application domain.

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

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

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"

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
Vorheriges Kapitel Deep Learning Approaches for IoT Security in the Big Data Era
Nächstes Kapitel The Utilization of Blockchain for Enhancing Big Data Security and Veracity
Literatur
1.
McGraw G, Morrisett G (2000) Attacking malicious code: a report to the infosec research council. IEEE Softw 17(5):33–41CrossRef
2.
Xufang L, Loh PKK, Tan F (2011) Mechanisms of polymorphic and metamorphic viruses. In 2011 European intelligence and security informatics conference (EISIC) 149–154
3.
4.
EroCarrera, Silberman P (2010) State of malware: family ties
5.
Egele M et al (2008) A survey on automated dynamic malware-analysis techniques and tools. ACM Comput Surv 44(2):1–42CrossRef
6.
Vinod P et al (2009) Survey on malware detection methods
7.
8.
Yin H et al (2007) Panorama: capturing system-wide information flow for malware detection and analysis. In: Proceedings of the 14th ACM conference on computer and communications security. ACM, Alexandria, pp 116–127
9.
Idika N, Mathur AP (2007) A survey of malware detection techniques
10.
Bostami B, Ahmed M, Choudhury S (2019) False data injection attacks in internet of things. In: Al-Turjman F (ed) Performability in internet of things. EAI/Springer innovations in communication and computing. Springer, Cham
11.
Beaucamps P (2007) Advanced polymorphic techniques. Int J Comput Sci 2(3):194–205
12.
Szor P (2005) The art of computer virus research and defense. Addison-Wesley Professional, Upper Saddle River
13.
Shah A (2010) Approximate disassembly using dynamic programming [PhD. Thesis], San Jose State University, US
14.
Szor P (1998) The Marburg situation. Virus Bull:8–10
15.
Filiol E (2005) Computer viruses: from theory to applications. Springer, ParisMATH
16.
Walenstein A, Mathur R, Chouchane M et al (2007) The design space of metamorphic malware. In: Proceedings of the 2nd international conference on information warfare and security (ICIW 2007), pp 241–248
17.
Lakhotia A, Kapoor A, Kumar E (2004) Are metamorphic viruses really invincible? Virus Bull:5–7
18.
Ferrie P, Corporation S, Monica S (2001) Hunting for metamorphic. Proceedings of the Virus Bulletin Conference 2001, Czech Republic, Prague, 2001 September 27–28, 123144
19.
Gutmann P (2007) The commercial malware industry
20.
Islam MDR, Tian R, Batten LM, Versteeg S (2013) Classification of malware based on integrated static and dynamic features. J Netw Comput Appl 36(2):646–656CrossRef
21.
22.
Jacob G, Debar H, Filiol E (2008) Behavioral detection of malware: from a survey towards established taxonomy. J Comput Virol 4(3):251–266CrossRef
23.
Hofmeyr S, Forrest S, Somayaji A (1998) Intrusion detection using sequences of system calls. J Comput Secur 6:151–180CrossRef
24.
Sato I, Okazaki Y, Goto S (2002) An improved intrusion detection method based on process profiling. IPSJ J 43:3316–3326
25.
Mohata VB (2013) Mobile malware detection techniques. Int J Comput Sci Eng Technol (IJCSET)
26.
Schultz M, Eskin E, Zadok E, Stolfo S (2001) Data mining methods for detection of new malicious executables. In IEEE symposium on security and privacy, pp 38–49. IEEE Computer Society
27.
Henchiri O, Japkowicz N (2006) A feature selection and evaluation scheme for computer virus detection. In: Proceedings of ICDM-2006, Hong Kong, pp 891–895
28.
Ye Y, Wang D, Li T, Ye D (2007) IMDS: intelligent malware detection system. In: Proceedings of the ACM international conference on knowledge discovery data mining, pp 1043–1047
29.
Ye Y, Li T, Jiang Q, Wang Y (2010) CIMDS: adapting post processing techniques of associative classification for malware detection. IEEE Trans Syst Man Cybern C 40(3):298–307CrossRef
30.
Jeong K, Lee H (2008) Code graph for malware detection. In information networking. In: ICOIN. International conference on, Jan 2008
31.
Lee J, Jeong K, Lee H (2010) Detecting metamorphic malwares using computing, ser. ACM, New York, pp 1970–1977
32.
Ye Y, Li T, Huang K, Jiang Q, Chen Y (2010) Hierarchical associative classifier (HAC) for malware detection from the large and imbalanced gray list. J Intell Inf Syst 35(1):1–20CrossRef
33.
Ahmed F, Hameed H, Shafiq MZ, Farooq M (2009) Using spatio-temporal information in API calls with machine learning algorithms for malware detection. In: AISec ‘09 Proceedings of the 2nd ACM workshop on Security and artificial intelligence, pp 55–62
34.
Bilar D (2007) OpCodes as predictor for malware. Int J Electron Secur Digit Forensics 1(2):156CrossRef
35.
Santos I, Brezo F, Nieves J, Penya Y (2010) Idea: OpCode-sequencebased malware detection. In: Engineering secure software and system. Springer, Berlin/Heidelberg
36.
Santos I, Brezo F, Ugarte-Pedrero X, Bringas PG (2011) OpCode sequences as representation of executables for data-mining-based unknown malware detection. Inf Sci 231:64–82MathSciNetCrossRef
37.
Santos I, Brezo F, Sanz B, Laorden C, Bringas PG (2011) Using opCode sequences in single-class learning to detect unknown malware. IET Inf Secur 5(4):220CrossRef
38.
Santos I, Laorden C, Bringas P (2011) Collective classification for unknown malware detection. In: Proceedings of the 6th ACM symposium on information, computer and communications security
39.
Santos I, Sanz B, Laorden C (2011) OpCode-sequence-based semisupervised unknown malware detection. In: Computational intelligence in security for information systems. Springer, Berlin/Heidelberg
40.
Runwal N, Low RM, Stamp M (2012) OpCode graph similarity and metamorphic detection. J Comput Virol 8(1–2):37–52CrossRef
41.
Shabtai A, Moskovitch R, Feher C, Dolev S, Elovici Y (2012) Detecting unknown malicious code by applying classification techniques on OpCode patterns. Secur Inf 1(1):1CrossRef
42.
Gerald GBS, Tesauro J, Kephart JO (1996) Neural network for computer virus recognition. IEEE Expert
43.
Arnold W, Tesauro G (2000) Automatically generated Win32 heuristic virus detection. In Virus Bulletin Conference
44.
Abou-assaleh, T, Cercone N, Keß V, Sweidan R (2004) N-gram-based detection of new malicious code, no. 1
45.
Maloof MA, Kolter JZ (2006) Learning to detect malicious executables in the wild. In roc of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
46.
Moskovitch EY, Stopel D, Feher C, Nissim N, Japkowicz N (2009) Unknown malcode detection and the imbalance problem. J Comput Virol 5(4):295–308CrossRef
47.
Bruschi D, Martignoni L, Monga M (2006) Detecting self-mutating malware using control-flow graph matching. In: Büschkes R, Laskov P (eds) Detection of intrusions and malware & vulnerability assessment, volume 4064 of LNCS. Springer, Berlin, pp 129–143
48.
Zhao Z (2011) A virus detection scheme based on features of control flow graph. 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), pp 943–947
49.
Bonfante G, Kaczmarek M, Marion JY (2007) Control flow graphs as malware signatures. WTCV
50.
Eskandari M, Hashemi S (2011) Metamorphic malware detection using control flow graph mining. Int J Comput Sci Netw Secur 11:1–6
51.
Kim K, Moon BR (2010) Malware detection based on dependency graph using hybrid genetic algorithm. In Proceedings of the 12th annual conference on Genetic and evolutionary computation, July 07–11, 2010
52.
Nataraj L, Karthikeyan S, Jacob G, Manjunath BS (2011) Malware images: visualization and automatic classification. In: Proceedings of the 8th international symposium on visualization for cyber security, VizSec ‘11. ACM.. ISBN 978-1-4503-0679-9, New York, pp 4:1–4:7. https://​doi.​org/​10.​1145/​2016904.​2016908CrossRef
53.
Dahl GE, Stokes JW, Deng L, Yu D (2013) Large-scale malware classification using random projections and neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on Acoustics. IEEE, 3422–3426
54.
Saxe J, Berlin K (2015) Deep neural network based malware detection using two dimensional binary program features. In 2015 10th International Conference on Malicious and Unwanted Software (MALWARE). IEEE
55.
Pascanu R, Stokes JW, Sanossian H, Marinescu M, Thomas A (2015) Malware classification with recurrent networks. In acoustics, speech and signal processing (ICASSP), 2015 IEEE International Conference on Acoustics. IEEE, 1916–1920
56.
Cakir B, Dogdu E (2018) Malware classification using deep learning methods. In: Proceedings of the ACMSE 2018 conference (ACMSE ‘18). ACM, New York. Article 10, 5 pages
57.
Raff E, Barker J, Sylvester J, Brandon R, Catanzaro B, Nicholas C (2017) Malware detection by eating a whole exe. arXiv preprint arXiv:1710.09435
58.
David OE, Netanyahu NS (2015) DeepSign: deep learning for automatic malware signature generation and classification. 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, 2015, pp 1–8
59.
Karbab E Debbabi M Derhab A Mouheb D (2017) Android malware detection using deep learning on API method sequences
60.
Choi S, Jang S, Kim Y, Kim J (2017) Malware detection using malware image and deep learning. 2017 International conference on information and communication technology convergence (ICTC), Jeju, 2017, pp 1193–1195
61.
Le Q, Boydell O, Mac Namee B, Scanlon M (2018) Deep learning at the shallow end: malware classification for non-domain experts. Digit Investig 26:S118–S126CrossRef
62.
Hardy W, Chen L, Hou S, Ye Y, Li X (2016) Dl4md: a deep learning framework for intelligent malware detection. Athens: The Steering Committee of The World Congress in computer science, computer engineering and applied computing (WorldComp), pp 61–67
63.
Huang W, Stokes JW (2016) MtNet: a multi-task neural network for dynamic malware classification. In: In Proc. of the 13th international conference on detection of intrusions and malware, and vulnerability assessment, DIMVA 2016. Springer, Cham, pp 399–418
64.
65.
66.
Kang H, Jang JW, Mohaisen A, Kim HK (2015) Detecting and classifying android malware using static analysis along with creator information. Int J Distrib Sens Netw 11(6):479174CrossRef
67.
Faruki P, Laxmi V, Bharmal A, Gaur MS, Ganmoor V (2015) AndroSimilar: robust signature for detecting variants of Android malware. J Inf Secur Appl 22:66–80
68.
Song J, Han C, Wang K, Zhao J, Ranjan R, Wang L (2016) An integrated static detection and analysis framework for Android. Pervasive Mob Comput 32:15–25CrossRef
69.
Sun M, Li X, Lui JC, Ma RT, Liang Z (2017) Monet: a user-oriented behavior-based malware variants detection system for Android. IEEE Trans Inf Forensics Secur 12(5):1103–1112CrossRef
70.
71.
Wu DJ, Mao CH, Wei TE, Lee HM, Wu KP (2012) DroidMat: android malware detection through manifest and API calls tracing. In: 2012 seventh Asia joint conference on information security (Asia JCIS), pp. 62–69. IEEE
72.
Talha KA, Alper DI, Aydin C (2015) APK auditor: permission-based Android malware detection system. Digit Investig 13:1–14CrossRef
73.
Sato R, Chiba D, Goto S (2013) Detecting Android malware by analyzing manifest files. Proc Asia Pac Adv Netw 36(23–31):17
74.
Ping X, Xiaofeng W, Wenjia N, Tianqing Z, Gang L (2014) Android malware detection with contrasting permission patterns. China Commun 11(8):1–14
75.
Vidal JM, Monge MAS, Villalba LJG (2018) A novel pattern recognition system for detecting Android malware by analyzing suspicious boot sequences. Knowl-Based Syst 150: 198–217CrossRef
76.
Canfora G, Mercaldo F, Visaggio CA (2016) An HMM and structural entropy based detector for android malware: an empirical study. Comput Secur 61:1–18CrossRef
77.
Karbab EB et al (2017) Android malware detection using deep learning on API method sequences. CoRR abs/1712.08996: n. Pag
78.
79.
Yuan Z, Lu Y, Xue Y (2016) Droiddetector: android malware characterization and detection using deep learning. Tsinghua Sci Technol 21(1):114–123CrossRef
Metadaten
Titel
Deep Learning Meets Malware Detection: An Investigation
verfasst von
Biozid Bostami
Mohiuddin Ahmed
Copyright-Jahr
2020
Buch
Combating Security Challenges in the Age of Big Data

Print ISBN: 978-3-030-35641-5

Electronic ISBN: 978-3-030-35642-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-35642-2_7