Unmasking the Lurking: Malicious Behavior Detection for IoT Malware with Multi-label Classification-Reference-Cited by-同舟云学术

Unmasking the Lurking: Malicious Behavior Detection for IoT Malware with Multi-label Classification

Published:2024-06-20 Issue: Volume: Page:
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Container-title:Proceedings of the 25th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems
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Author:

Feng Ruitao¹^ORCID,Li Sen²^ORCID,Chen Sen²^ORCID,Ge Mengmeng³^ORCID,Li Xuewei²^ORCID,Li Xiaohong²^ORCID

Affiliation:

1. Singapore Management University, Singapore, Singapore

2. Tianjin University, Tianjin, China

3. Nanyang Technological University, Singapore, Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652032.3657577

Reference54 articles.

1. Analyzing and Detecting Emerging Internet of Things Malware: A Graph-Based Approach

2. Efficient Signature Generation for Classifying Cross-Architecture IoT Malware

3. Omar Alrawi, Charles Lever, Kevin Valakuzhy, Kevin Snow, Fabian Monrose, and Manos Antonakakis. 2021. The Circle of life: A $large-scale$ study of the $IoT$ malware lifecycle. In 30th USENIX Security Symposium (USENIX Security 21). 3505–3522.

4. Kishore Angrishi. 2017. Turning internet of things (iot) into internet of vulnerabilities (iov): Iot botnets. arXiv preprint arXiv:1702.03681.

5. Manos Antonakakis, Tim April, Michael Bailey, Matt Bernhard, Elie Bursztein, Jaime Cochran, Zakir Durumeric, J Alex Halderman, Luca Invernizzi, and Michalis Kallitsis. 2017. Understanding the mirai botnet. In 26th USENIX security symposium (USENIX Security 17). 1093–1110.

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