Malware squid: a novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation

Robert Shire; Stavros Shiaeles; Keltoum Bendiab; Bogdan Ghita; Nicholas Kolokotronis

doi:10.1007/978-3-030-30859-9_6

Malware squid: a novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation

Robert Shire
, Stavros Shiaeles^*
, Keltoum Bendiab
, Bogdan Ghita
, Nicholas Kolokotronis

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

438 Downloads (Pure)

Abstract

Internet of Things devices have seen a rapid growth and popularity in recent years with many more ordinary devices gaining network capability and becoming part of the ever growing IoT network. With this exponential growth and the limitation of resources, it is becoming increasingly harder to protect against security threats such as malware due to its evolving faster than the defence mechanisms can handle with. The traditional security systems are not able to detect unknown malware as they use signature-based methods. In this paper, we aim to address this issue by introducing a novel IoT malware traffic analysis approach using neural network and binary visualisation. The prime motivation of the proposed approach is to faster detect and classify new malware (zero-day malware). The experiment results show that our method can satisfy the accuracy requirement of practical application.

Original language	English
Title of host publication	Internet of Things, Smart Spaces, and Next Generation Networks and Systems
Subtitle of host publication	19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings
Editors	Olga Galinina, Sergey Andreev, Sergey Balandin, Yevgeni Koucheryavy
Publisher	Springer
Pages	65-76
Number of pages	12
ISBN (Electronic)	978-3-030-30859-9
ISBN (Print)	978-3-030-30858-2
DOIs	https://doi.org/10.1007/978-3-030-30859-9_6
Publication status	Published - 12 Sept 2019
Event	19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019 - St. Petersburg, Russian Federation Duration: 26 Aug 2019 → 28 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11660
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019
Country/Territory	Russian Federation
City	St. Petersburg
Period	26/08/19 → 28/08/19

Keywords

Binary visualization
Intrusion detection system
Network anomaly detection
Neural network
Traffic analysis

Access to Document

10.1007/978-3-030-30859-9_6

Malware squid
The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-30859-9_6.
Accepted author manuscript (Post-print), 426 KB

Cite this

Shire, R., Shiaeles, S., Bendiab, K., Ghita, B., & Kolokotronis, N. (2019). Malware squid: a novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation. In O. Galinina, S. Andreev, S. Balandin, & Y. Koucheryavy (Eds.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings (pp. 65-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11660). Springer. https://doi.org/10.1007/978-3-030-30859-9_6

Shire, Robert ; Shiaeles, Stavros ; Bendiab, Keltoum et al. / Malware squid: a novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation. Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. editor / Olga Galinina ; Sergey Andreev ; Sergey Balandin ; Yevgeni Koucheryavy. Springer, 2019. pp. 65-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Internet of Things devices have seen a rapid growth and popularity in recent years with many more ordinary devices gaining network capability and becoming part of the ever growing IoT network. With this exponential growth and the limitation of resources, it is becoming increasingly harder to protect against security threats such as malware due to its evolving faster than the defence mechanisms can handle with. The traditional security systems are not able to detect unknown malware as they use signature-based methods. In this paper, we aim to address this issue by introducing a novel IoT malware traffic analysis approach using neural network and binary visualisation. The prime motivation of the proposed approach is to faster detect and classify new malware (zero-day malware). The experiment results show that our method can satisfy the accuracy requirement of practical application.",

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author = "Robert Shire and Stavros Shiaeles and Keltoum Bendiab and Bogdan Ghita and Nicholas Kolokotronis",

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note = "19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019 ; Conference date: 26-08-2019 Through 28-08-2019",

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Shire, R, Shiaeles, S , Bendiab, K, Ghita, B & Kolokotronis, N 2019, Malware squid: a novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation. in O Galinina, S Andreev, S Balandin & Y Koucheryavy (eds), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11660, Springer, pp. 65-76, 19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019, St. Petersburg, Russian Federation, 26/08/19. https://doi.org/10.1007/978-3-030-30859-9_6

Malware squid: a novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation. / Shire, Robert; Shiaeles, Stavros ; Bendiab, Keltoum et al.
Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. ed. / Olga Galinina; Sergey Andreev; Sergey Balandin; Yevgeni Koucheryavy. Springer, 2019. p. 65-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11660).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Shiaeles, Stavros

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AU - Ghita, Bogdan

AU - Kolokotronis, Nicholas

PY - 2019/9/12

Y1 - 2019/9/12

N2 - Internet of Things devices have seen a rapid growth and popularity in recent years with many more ordinary devices gaining network capability and becoming part of the ever growing IoT network. With this exponential growth and the limitation of resources, it is becoming increasingly harder to protect against security threats such as malware due to its evolving faster than the defence mechanisms can handle with. The traditional security systems are not able to detect unknown malware as they use signature-based methods. In this paper, we aim to address this issue by introducing a novel IoT malware traffic analysis approach using neural network and binary visualisation. The prime motivation of the proposed approach is to faster detect and classify new malware (zero-day malware). The experiment results show that our method can satisfy the accuracy requirement of practical application.

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KW - Intrusion detection system

KW - Network anomaly detection

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SN - 978-3-030-30858-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Koucheryavy, Yevgeni

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Shire R, Shiaeles S , Bendiab K, Ghita B, Kolokotronis N. Malware squid: a novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation. In Galinina O, Andreev S, Balandin S, Koucheryavy Y, editors, Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. Springer. 2019. p. 65-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-30859-9_6

Malware squid: a novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation

Abstract

Publication series

Conference

Keywords

Access to Document

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