Hiding Malware inside a model of a neural network

From securityaffairs.co

neural network

Researchers Zhi Wang, Chaoge Liu, and Xiang Cui presented a technique to deliver malware through neural network models to evade the detection without impacting the performance of the network.

Tests conducted by the experts demonstrated how to embed 36.9MB of malware into a 178MB-AlexNet model within 1% accuracy loss, this means that the threat is completely transparent to antivirus engines.

Experts believe that with the massive adoption of artificial intelligence, malware authors will look with an increasing intered in the use of neural networks. We hope this work could provide a referenceable scenario for the defense on neural network-assisted attacks.

Read more…