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Analysis of Neural Network Detectors for Network Attacks



Qingtian Zou, Lan Zhang, Anoop Singhal, Xiaoyan Sun, Peng Liu


While network attacks play a critical role in many advanced persistent threat (APT) campaigns, an arms race exists between the network defenders and the adversary: to make APT campaigns stealthy, the adversary is strongly motivated to evade the detection system. However, new studies have shown that neural network is likely a game-changer in the arms race: neural network could be applied to achieve accurate, signature-free, and low-false-alarm-rate detection. In this work, we investigate whether the adversary could fight back during the next phase of the arms race. In particular, noticing that none of the existing adversarial example generation methods could generate malicious packets (and sessions) that can simultaneously compromise the target machine and evade the neural network detection model, we propose a novel attack method to achieve this goal. We have designed and implemented the new attack. We have also used Address Resolution Protocol (ARP) Poisoning and Domain Name System (DNS) Cache Poisoning as the case study to demonstrate the effectiveness of the proposed attack.
Journal of Computer Security


Network Attack, Neural Network, Adversarial Example


Zou, Q. , Zhang, L. , Singhal, A. , Sun, X. and Liu, P. (2023), Analysis of Neural Network Detectors for Network Attacks, Journal of Computer Security, [online],, (Accessed June 24, 2024)


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Created November 15, 2023, Updated January 8, 2024