EvilModel 2.0: Bringing Neural Network Models into Malware Attacks

Zhi Wang

doi:10.1016/j.cose.2022.102807

EvilModel 2.0: Bringing Neural Network Models into Malware Attacks

2022-09-01

6

OA

AI

DOI：10.1016/j.cose.2022.102807

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Security issues have gradually emerged with the continuous development of artificial intelligence (AI). Earlier work verified the possibility of converting neural network models into stegomalware, embedding malware into a model with limited impact on the model's performance. However, existing methods are not applicable in real-world attack scenarios and do not attract enough attention from the security com-munity due to performance degradation and additional workload. Therefore, we propose an improved stegomalware EvilModel. By analyzing the composition of the neural network model, three new meth-ods for embedding malware into the model are proposed: MSB reservation, fast substitution, and half substitution, which can embed malware that accounts for half of the model's volume without affecting the model's performance. We built 550 EvilModels using ten mainstream neural network models and 19 malware samples. The experiment shows that EvilModel achieved an embedding rate of 48.52%. A quantitative algorithm is proposed to evaluate the existing embedding methods. We also design a trigger and propose a threat scenario for the targeted attack. The practicality and effectiveness of the proposed methods were demonstrated by experiments and analyses of the embedding capacity, performance im-pact, and detection evasion.(c) 2022 Elsevier Ltd. All rights reserved.

Keyword:

Neuralnetwork

Malware

AI-Poweredattack

Networksecurity

Steganography

EvilModel 2.0: Bringing Neural Network Models into Malware Attacks

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