Federated learning-based intelligent detection of DDoS attacks on ultra-dense networks

Rong Chang; ZhengXiong Mao; ChuanXu Yang; Fu Bao; Yuan Tian; Hang Zhang

doi:10.1117/12.2660925

2 February 2023 Federated learning-based intelligent detection of DDoS attacks on ultra-dense networks

Rong Chang, ZhengXiong Mao, ChuanXu Yang, Fu Bao, Yuan Tian, Hang Zhang

Proceedings Volume 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022); 124622J (2023) https://doi.org/10.1117/12.2660925
Event: International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 2022, Xi'an, China

Abstract

Some intelligent detection methods for ultra-dense network attacks are likely to generate false alarms in the application process. In order to improve the security in ultra-dense network, an intelligent detection method based on edition learning is designed. Considering the SRP change rate, different thresholds are set, the node switching structural features of ultra-dense networks are extracted, the function sets that can effectively control error detection are selected, the host recognition algorithm is designed, the function field selection model based on joint learning is constructed, the iteration points are created in the feasible domain, real-time network traffic is collected, and the doattack intelligent detection model is optimized. Experimental results: in the paper, the average probability of non-intelligent detection methods for attacks in ultra-dense networks is 24.864%, which shows that when combined with federated learning algorithm, it has more advantages in practical performance.

Citation Download Citation

Rong Chang, ZhengXiong Mao, ChuanXu Yang, Fu Bao, Yuan Tian, and Hang Zhang "Federated learning-based intelligent detection of DDoS attacks on ultra-dense networks", Proc. SPIE 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 124622J (2 February 2023); https://doi.org/10.1117/12.2660925

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