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A Deep-Learning Based Robust Framework Against Adversarial P.E. And Cryptojacking Malware, Faraz Amjad Naseem
A Deep-Learning Based Robust Framework Against Adversarial P.E. And Cryptojacking Malware, Faraz Amjad Naseem
FIU Electronic Theses and Dissertations
This graduate thesis introduces novel, deep-learning based frameworks that are resilient to adversarial P.E. and cryptojacking malware. We propose a method that uses a convolutional neural network (CNN) to classify image representations of malware, that provides robustness against numerous adversarial attacks. Our evaluation concludes that the image-based malware classifier is significantly more robust to adversarial attacks than a state-of-the-art ML-based malware classifier, and remarkably drops the evasion rate of adversarial samples to 0% in certain attacks. Further, we develop MINOS, a novel, lightweight cryptojacking detection system that accurately detects the presence of unwarranted mining activity in real-time. MINOS can detect …