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<p>Deep learning (DL) has found extensive applications in network traffic classification. To ensure the trustworthiness of these DL-based classifiers in real-world deployments, it is imperative to examine their susceptibility to attacks targeting AI models, similar to those encountered in other domains such as computer vision. Existing research has demonstrated that neural backdoor attacks pose a threat to DL-based network flow classifiers. However, these attack methods typically employ static trigger modes or necessitate attackers to simultaneously control both training data and the training process, imposing stringent conditions for launching these attacks in real-world scenarios. Additionally, current research overlooks the restoration process of trojan packets to their original data packets after introducing triggers. This omission poses a challenge for malicious recipients in effectively utilizing transmitted data for subsequent attack processes after receiving trojan packets. To address these issues, this paper proposes <i>Restorable Trojan Packet</i>, a practical packet-level network traffic neural backdoor attack method. Specifically, we design an encoder-decoder network. The encoder is employed to embed dynamic and sample-specific triggers into the payload of network packets, thereby bypassing detection by network traffic classifiers. The decoder is used to restore the packets with embedded triggers back to their original form, which allows attackers to proceed with subsequent attack steps after the reception of the trojan packets. The attacker only needs to perturb a small amount of training data, without requiring control over the entire training process, to efficiently poison the target model. Extensive experiments demonstrate that our attack method can effectively compromise packet-level network flow classifiers. The method achieves a high success rate with lower requirements on the attacker’s capabilities, all while maintaining high levels of stealthiness and robustness. Furthermore, our method accurately restores network packets to their original form.</p>

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Restorable Trojan Packet: a practical neural backdoor attack against network traffic classifiers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月27日

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  • Jiaji Yuan,
  • Mohan Li,
  • Yanbin Sun,
  • Xiang Yu

摘要

Deep learning (DL) has found extensive applications in network traffic classification. To ensure the trustworthiness of these DL-based classifiers in real-world deployments, it is imperative to examine their susceptibility to attacks targeting AI models, similar to those encountered in other domains such as computer vision. Existing research has demonstrated that neural backdoor attacks pose a threat to DL-based network flow classifiers. However, these attack methods typically employ static trigger modes or necessitate attackers to simultaneously control both training data and the training process, imposing stringent conditions for launching these attacks in real-world scenarios. Additionally, current research overlooks the restoration process of trojan packets to their original data packets after introducing triggers. This omission poses a challenge for malicious recipients in effectively utilizing transmitted data for subsequent attack processes after receiving trojan packets. To address these issues, this paper proposes Restorable Trojan Packet, a practical packet-level network traffic neural backdoor attack method. Specifically, we design an encoder-decoder network. The encoder is employed to embed dynamic and sample-specific triggers into the payload of network packets, thereby bypassing detection by network traffic classifiers. The decoder is used to restore the packets with embedded triggers back to their original form, which allows attackers to proceed with subsequent attack steps after the reception of the trojan packets. The attacker only needs to perturb a small amount of training data, without requiring control over the entire training process, to efficiently poison the target model. Extensive experiments demonstrate that our attack method can effectively compromise packet-level network flow classifiers. The method achieves a high success rate with lower requirements on the attacker’s capabilities, all while maintaining high levels of stealthiness and robustness. Furthermore, our method accurately restores network packets to their original form.