DIAMOND: A Structured Coevolution Feature Optimization Method for LDDoS Detection in SDN‐IoT

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Abstract
Software‐defined networking for IoT (SDN‐IoT) has become popular owing to its utility in smart applications. However, IoT devices are limited in computing resources, which makes them vulnerable to Low‐rate Distributed Denial of Service (LDDoS). It is worth noting that LDDoS attacks are extremely stealthy and can evade the monitoring of traditional detection methods. Therefore, how to choose the optimal features to improve the detection performance of LDDoS attack detection methods is a key problem. In this paper, we propose DIAMOND, a structured coevolution feature optimization method for LDDoS detection in SDN‐IoT. DIAMOND is consisted of a reachable count sorting clustering algorithm, a group structuring method, a comutation strategy, and a cocrossover strategy. By analysing the information of SDN‐IoT network features in the solution space, the relationship between different SDN‐IoT network features and the optimal solution is explored in DIAMOND. Then, the individuals with associated SDN‐IoT network features are divided into different subpopulations, and a structural tree is generated. Further, multiple structural trees evolve in concert with each other. The evaluation results show that DIAMOND can effectively select optimal low‐dimension feature sets and improve the performance of the LDDoS detection method, in terms of detection precision and response time.
Funding Information
  • Guizhou University ((2019)52)
  • National Natural Science Foundation of China (62102111, 61902085)

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