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.
Highlights
Internet of Things (IoT) ecosystem is one of the most critical aspects of human lives, which facilitates a wide variety of applications in different domains such as smart homes, agriculture, healthcare, smart cities, smart grids, industrial automation (Industry 4.0), smart driving, and elderly assistance [1]
LDDoS (Low-rate Distributed Denial of Service) attack is a serious threat for Software-defined networking for IoT (SDN-IoT), which exploits the vulnerabilities in network protocols to launch attacks and often realizes superior attack effects at a smaller attack cost
The main contributions are concluded as below: (1) By dividing the population into several subpopulations based on the proposed reachable count sorting clustering algorithm and a group structuring method and executing coevolution based on the designed comutation strategy and cocrossover strategy, DIAMOND is proposed
Summary
Internet of Things (IoT) ecosystem is one of the most critical aspects of human lives, which facilitates a wide variety of applications in different domains such as smart homes, agriculture, healthcare, smart cities, smart grids, industrial automation (Industry 4.0), smart driving, and elderly assistance [1]. The computing resources, storage resources, and network capacity of IoT devices are limited to carry high-speed data transmission. SDN-IoT is proposed to improve transmission quality [2]. In SDN-IoT, SDN changes the limitation of the network infrastructure, gives the network more flexibility, and simplifies policy implementation and network configuration [3]. LDDoS (Low-rate Distributed Denial of Service) attack is a serious threat for SDN-IoT, which exploits the vulnerabilities in network protocols to launch attacks and often realizes superior attack effects at a smaller attack cost. In order to overcome the shortcomings mentioned above, a structured coevolution feature optimization method for LDDoS detection in SDN-IoT (DIAMOND) is proposed. (1) By dividing the population into several subpopulations based on the proposed reachable count sorting clustering algorithm and a group structuring method and executing coevolution based on the designed comutation strategy and cocrossover strategy, DIAMOND is proposed (2) A reachable count sorting clustering algorithm (BONNET) is designed to divide the population into subpopulations with different SDN-IoT network feature information, and each subpopulation is considered as a suboptimal solution set in the solution space (3) A group structuring method is designed to further structure a subpopulation into structural trees in order to sort the individuals in the subpopulation orderly based on SDN-IoT network information, where multiple structural trees evolve in concert (4) A comutation strategy is proposed based on the optimal subpopulation guidance direction, amount of information on SDN-IoT network features, and the evolutionary trajectory of the subpopulation, to move the individual towards the optimal solution in an orderly manner (5) A cocrossover strategy is designed to facilitate information exchange between different structural trees by exchanging individuals between different structural trees and the genes of individuals in the same tree
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