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Malware detection is one central topic in cybersecurity, which ideally requires an accurate, efficient and robust (to malware variants) solution. In this work, we propose a hardwareassisted architecture to perform online malware detection with two phases. In the offline phase, we learn the attack model of malware in the form of Deterministic Finite Automaton (DFA). During the runtime phase, we implement...
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