This post summarizes the work we presented at the 2026 IEEE 4th Conference on Information Technology and Data Science (CITDS 2026) in Debrecen, Hungary. My name is József Sándor, and I’m a PhD student in the CrySyS Lab. FAME is joint work with Endre Glocker and Roland Nagy.
Machine learning (ML) has become a popular tool for detecting malware on IoT devices. These devices have very little CPU, memory, and energy to spare, so detectors need to be lightweight. One such detector is SIMBIoTA-ML, which feeds the TLSH similarity hash of a file into a classifier. It is cheap to compute and detects new malware that resembles what the model saw in training.
But like every ML-based detector, SIMBIoTA-ML can be fooled by adversarial examples (AE). In our paper we ask: can we build a systematic, reliable pipeline that produces functional evasive malware against it? The answer is FAME (From Adversarial feature vectors to Malware Evasion).
Continue reading FAME: From Adversarial Feature Vectors to Malware Evasion