Abstract
Botnets are one of the most serious cybersecurity threats facing organizations today. Although the analysis and detection of botnets have achieved a lot of research results, it still has problems such as strong concealment and difficult identification. Therefore, we propose a botnet detection method based on NSA and DRN. This method uses our improved NSA to expand the preprocessed and dimensionally reduced malicious traffic data with fewer samples, and then extracts useful features of network traffic from two dimensions through SENet-based DRN combined with BiGRU. Experimental results based on the CICIDS-2017 and UNSW-NB15 datasets show that our proposed method has a high accuracy for botnet detection and improves the detection accuracy of rare malicious traffic 99.99% and 99.96%. In addition, we further demonstrate the good generalization ability and robustness of our method in botnet detection through an ablation study.
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Acknowledgement
This research was supported by Sichuan Science and Technology Program (No. 2022YFG0339).
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Yin, Z., Qin, R., Ye, C., He, F., Zhang, L. (2024). Botnet Detection Method Based on NSA and DRN. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1968. Springer, Singapore. https://doi.org/10.1007/978-981-99-8181-6_40
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DOI: https://doi.org/10.1007/978-981-99-8181-6_40
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