Sessionvideo: A Novel Approach for Encrypted Traffic Classification via 3D-CNN Model

Wang, Haiyang; Xu, Tongge; Yang, Jian; Wu, Ling; Yang, Liqun

doi:10.23919/apnoms56106.2022.9919917

Cited by 4 publications

(2 citation statements)

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“…Although the backdoor attack has been extensively researched in multiple applications, such as computer vision (CV) [11][12][13][14][15][16][17] and natural language processing (NLP) [18][19][20][21][22][23][24][25][26][27][28][29], there is no research in the field of DGA detection. Due to the particularity of DGA , existing backdoor attacks can not be directly applied to the DGA detection approach based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

BadDGA: Backdoor Attack on LSTM-Based Domain Generation Algorithm Detector

Zhai

Yang

et al. 2023

Electronics

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Due to the outstanding performance of deep neural networks (DNNs), many researchers have begun to transfer deep learning techniques to their fields. To detect algorithmically generated domains (AGDs) generated by domain generation algorithm (DGA) in botnets, a long short-term memory (LSTM)-based DGA detector has achieved excellent performance. However, the previous DNNs have found various inherent vulnerabilities, so cyberattackers can use these drawbacks to deceive DNNs, misleading DNNs into making wrong decisions. Backdoor attack as one of the popular attack strategies strike against DNNs has attracted widespread attention in recent years. In this paper, to cheat the LSTM-based DGA detector, we propose BadDGA, a backdoor attack against the LSTM-based DGA detector. Specifically, we offer four backdoor attack trigger construction methods: TLD-triggers, Ngram-triggers, Word-triggers, and IDN-triggers. Finally, we evaluate BadDGA on ten popular DGA datasets. The experimental results show that under the premise of 1‰ poisoning rate, our proposed backdoor attack can achieve a 100% attack success rate to verify the effectiveness of our method. Meanwhile, the model’s utility on clean data is influenced slightly.

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Section: Introductionmentioning

confidence: 99%

BadDGA: Backdoor Attack on LSTM-Based Domain Generation Algorithm Detector

Zhai

Yang

et al. 2023

Electronics

Self Cite

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“…Therefore, the accurate classification of network traffic has always been a hot topic in the field. In recent years, deep learning has achieved great success in the field of traffic classification [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

DRnet: Dynamic Retraining for Malicious Traffic Small-Sample Incremental Learning

et al. 2023

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Deep learning has achieved good classification results in the field of traffic classification in recent years due to its good feature representation ability. However, the existing traffic classification technology cannot meet the requirements for the incremental learning of tasks in online scenarios. In addition, due to the high concealment and fast update speed of malicious traffic, the number of labeled samples that can be captured is scarce, and small samples cannot drive neural network training, resulting in poor performance of the classification model. Therefore, this paper proposes an incremental learning method for small-sample malicious traffic classification. The method uses the pruning strategy to find the redundant network structure and dynamically allocates redundant neurons for training based on the proposed measurement method according to the difficulty of the new class. This enables the network to perform incremental learning without excessively consuming storage and computing resources, and reasonable allocation improves the classification accuracy of new classes. At the same time, through the knowledge transfer method, the model can reduce the catastrophic forgetting of the old class, relieve the pressure of training large parameters with small-sample data, and improve the model classification performance. Experiments involving multiple datasets and settings show that our method is superior to the established baseline in terms of classification accuracy, consuming 50% less memory.

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