EvilModel 2.0: Hiding Malware Inside of Neural Network Models

• URL: http://arxiv.org/abs/2109.04344v1
• Date: Thu, 9 Sep 2021 15:31:33 GMT
• Title: EvilModel 2.0: Hiding Malware Inside of Neural Network Models
• Authors: Zhi Wang, Chaoge Liu, Xiang Cui, Jie Yin
• Abstract summary: Turning neural network models into stegomalware is a malicious use of AI. Existing methods have a low malware embedding rate and a high impact on the model performance. This paper proposes new methods to embed malware in models with high capacity and no service quality degradation.
• Score: 7.060465882091837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While artificial intelligence (AI) is widely applied in various areas, it is also being used maliciously. It is necessary to study and predict AI-powered attacks to prevent them in advance. Turning neural network models into stegomalware is a malicious use of AI, which utilizes the features of neural network models to hide malware while maintaining the performance of the models. However, the existing methods have a low malware embedding rate and a high impact on the model performance, making it not practical. Therefore, by analyzing the composition of the neural network models, this paper proposes new methods to embed malware in models with high capacity and no service quality degradation. We used 19 malware samples and 10 mainstream models to build 550 malware-embedded models and analyzed the models' performance on ImageNet dataset. A new evaluation method that combines the embedding rate, the model performance impact and the embedding effort is proposed to evaluate the existing methods. This paper also designs a trigger and proposes an application scenario in attack tasks combining EvilModel with WannaCry. This paper further studies the relationship between neural network models' embedding capacity and the model structure, layer and size. With the widespread application of artificial intelligence, utilizing neural networks for attacks is becoming a forwarding trend. We hope this work can provide a reference scenario for the defense of neural network-assisted attacks.

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