September 25, 2021

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EvilModel: Hiding Malware Inside of Neural Network Models. (arXiv:2107.08590v4 [cs.CR] UPDATED)

Delivering malware covertly and evasively is critical to advanced malware
campaigns. In this paper, we present a new method to covertly and evasively
deliver malware through a neural network model. Neural network models are
poorly explainable and have a good generalization ability. By embedding malware
in neurons, the malware can be delivered covertly, with minor or no impact on
the performance of neural network. Meanwhile, because the structure of the
neural network model remains unchanged, it can pass the security scan of
antivirus engines. Experiments show that 36.9MB of malware can be embedded in a
178MB-AlexNet model within 1% accuracy loss, and no suspicion is raised by
anti-virus engines in VirusTotal, which verifies the feasibility of this
method. With the widespread application of artificial intelligence, utilizing
neural networks for attacks becomes a forwarding trend. We hope this work can
provide a reference scenario for the defense on neural network-assisted
attacks.