The tangled genealogy of IoT malware

Cozzi, Emanuele; Vervier, Pierre-Antoine; Dell'Amico, Matteo; Shen, Yun; Bilge, Leyla; Balzarotti, Davide

The recent emergence of consumer off-the-shelf embedded (IoT) devices and the rise of large-scale IoT botnets has dramatically increased the volume and sophistication of Linux malware observed in the wild. The security community has put a lot of effort to document these threats but analysts mostly rely on manual work, which makes it difficult to scale and hard to regularly maintain. Moreover, the vast amount of code reuse that characterizes IoT malware calls for an automated approach to detect similarities and identify the phylogenetic tree of each family. In this paper we present the largest measurement of IoT malware to date. We systematically reconstruct – through the use of binary code similarity – the lineage of IoT malware families, and track their relationships, evolution, and variants. We apply our technique on a dataset of more than 93k samples submitted to VirusTotal over a period of 3.5 years. We discuss the findings of our analysis and present several case studies to highlight the tangled relationships of IoT malware.

Sécurité numérique
Eurecom Ref:
© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in