Master of Science (MS)
First Advisor's Name
Dr. Selcuk Uluagac
First Advisor's Committee Title
Second Advisor's Name
Dr. Kemal Akkaya
Second Advisor's Committee Title
Third Advisor's Name
Dr. Alexander Perez-Pons
Third Advisor's Committee Title
P.E. malware, cryptojacking, deep-learning, adversarial machine learning, web assembly
Date of Defense
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 mining activity with a low TNR and FPR, in an average of 25.9 milliseconds while using a maximum of 4% of CPU and 6.5% of RAM. Therefore, it can be concluded that the frameworks presented in this thesis attain high accuracy, are computationally inexpensive, and are resistant to adversarial perturbations.
Naseem, Faraz Amjad, "A Deep-Learning Based Robust Framework Against Adversarial P.E. and Cryptojacking Malware" (2020). FIU Electronic Theses and Dissertations. 4459.
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