Can I read Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses on EtoBox?

Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses by Abusnaina, Ahmed (author);Anwar, Afsah (author);Alshamrani, Sultan (author);Alabduljabbar, Abdulrahman (author);Jang, RhongHo (author);Nyang, DaeHun (author);Mohaisen, David (author) is a scholarly article available to read on EtoBox.

What is Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses about?

The rapid growth of the Internet of Things (IoT) devices is paralleled by them being on the front-line of malicious attacks. This has led to an explosion in the number of IoT malware, with continued mutations, evolution, and sophistication. Malware samples are detected using machine learning (ML) algorithms alongside the traditional signature-based methods. Although ML-based detectors improve the detection performance, they are susceptible to malware evolution and sophistication, making them limited to the patterns that they have been trained upon. This continuous trend motivates large body of literature on malware analysis and detection research, with many systems emerging constantly, outperforming their predecessors. In this paper, we systematically examine the state-of-the-art malware detection approaches, that utilize various representation and learning techniques, under a range of adversarial settings. Our analyses highlight the instability of the proposed detectors in learning patterns that distinguish the benign from the malicious software. The results exhibit that software mutations with functionality-preserving operations, such as stripping and padding, significantly deter

Author

Abusnaina, Ahmed (author);Anwar, Afsah (author);Alshamrani, Sultan (author);Alabduljabbar, Abdulrahman (author);Jang, RhongHo (author);Nyang, DaeHun (author);Mohaisen, David (author)

Publisher

ACM

Published

2022

Language

EN