Perturbing Vulnerable Bytes in Packets to Generate Adversarial Samples Resisting DNN-Based Traffic Monitoring

Leveraging the advanced capabilities of Deep Neural Networks (DNNs), attackers can precisely detect users' online activities through traffic monitoring, nullifying the efficacy of current encrypted communication tools/protocols and progressively resulting in privacy leakage. Several defensive methods against DNN-based traffic monitoring (DTM) have been proposed; however, these methods often rely excessively on prior knowledge and incur inevitable additional bandwidth overhead (BWO). Moreover, they frequently generate invalid packets that violate network transmission constraints. To address these drawbacks, in this paper, we propose BYTEFLIPPING, a byte-space grey-box defensive method, which perturbs vulnerable bytes in the transport layer payload to generate adversarial sample packets. We design a Payload Byte Vulnerability Ranking algorithm to pinpoint the most vulnerable bytes and based on this generate adversarial packets to defend DTM. Extensive experiments reveal that ByteFLIPPING performs well in protecting against three DTM methods across two benchmark datasets, significantly decreasing the accuracy of the state-of-the-art ET-BERT by 94%. Compared to baseline defensive methods, BYTEFLIPPING incurs no extra BWO, offers more dependable packet validity, and boasts greater feasibility.