Faraz Naseem (Florida International University), Ahmet Aris (Florida International University), Leonardo Babun (Florida International University), Ege Tekiner (Florida International University), A. Selcuk Uluagac (Florida International University)

Emerging WebAssembly(Wasm)-based cryptojacking malware covertly uses the computational resources of users without their consent or knowledge. Indeed, most victims of this malware are unaware of such unauthorized use of their computing power due to techniques employed by cryptojacking malware authors such as CPU throttling and obfuscation. A number of dynamic analysis-based detection mechanisms exist that aim to circumvent such techniques. However, since these mechanisms use dynamic features, the collection of such features, as well as the actual detection of the malware, require that the cryptojacking malware run for a certain amount of time, effectively mining for that period, and therefore causing significant overhead. To solve these limitations, in this paper, we propose MINOS, a novel, extremely lightweight cryptojacking detection system that uses deep learning techniques to accurately detect the presence of unwarranted Wasm-based mining activity in real-time. MINOS uses an image-based classification technique to distinguish between benign webpages and those using Wasm to implement unauthorized mining. Specifically, the classifier implements a convolutional neural network (CNN) model trained with a comprehensive dataset of current malicious and benign Wasm binaries. MINOS achieves exceptional accuracy with a low TNR and FPR. Moreover, our extensive performance analysis of MINOS shows that the proposed detection technique can detect mining activity instantaneously from the most current in-the-wild cryptojacking malware with an accuracy of 98.97%, in an average of 25.9 milliseconds while using a maximum of 4% of the CPU and 6.5% of RAM, proving that MINOS is highly effective while lightweight, fast, and computationally inexpensive.

View More Papers

V2X Security: Status and Open Challenges

Jonathan Petit (Director Of Engineering at Qualcomm Technologies) Dr. Jonathan Petit is Director of Engineering at Qualcomm Technologies, Inc., where he leads research in security of connected and automated vehicles (CAV). His team works on designing security solutions, but also develops tools for automotive penetration testing and builds prototypes. His recent work on misbehavior protection…

Read More

“Lose Your Phone, Lose Your Identity”: Exploring Users’ Perceptions...

Michael Lutaaya, Hala Assal, Khadija Baig, Sana Maqsood, Sonia Chiasson (Carleton University)

Read More

Practical Blind Membership Inference Attack via Differential Comparisons

Bo Hui (The Johns Hopkins University), Yuchen Yang (The Johns Hopkins University), Haolin Yuan (The Johns Hopkins University), Philippe Burlina (The Johns Hopkins University Applied Physics Laboratory), Neil Zhenqiang Gong (Duke University), Yinzhi Cao (The Johns Hopkins University)

Read More