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 proceedingConference contribution

281 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 languageEnglish
Title of host publicationInternet of Things, Smart Spaces, and Next Generation Networks and Systems
Subtitle of host publication19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings
EditorsOlga Galinina, Sergey Andreev, Sergey Balandin, Yevgeni Koucheryavy
PublisherSpringer Verlag
Pages65-76
Number of pages12
ISBN (Electronic)978-3-030-30859-9
ISBN (Print)978-3-030-30858-2
DOIs
Publication statusPublished - 12 Sep 2019
Event19th 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 201928 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11660
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th 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/TerritoryRussian Federation
CitySt. Petersburg
Period26/08/1928/08/19

Keywords

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

Fingerprint

Dive into the research topics of 'Malware squid: a novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation'. Together they form a unique fingerprint.

Cite this