A hybrid metaheuristic algorithm with machine learning for detecting denial-of-service attacks in wireless sensor networks

Denial-of-service (DoS) attacks pose a major threat to various kinds of computer networks. There are several kinds of networks that are victims of DoS attacks, one of them being the wireless sensor network (WSN). The main objective of this work is to detect such attacks in wireless sensor networks. These networks are susceptible to intrusion attacks because of their fragile defense mechanisms in unattended environments. Thus, a suitable intrusion detection system must be created to optimally detect DoS attacks and prevent them. This work proposes a hybrid technique called Grasshopper Optimization Algorithm-Genetic Algorithm (GOA-GA), which combines the advantages of two metaheuristic algorithms, namely, the Grasshopper Optimization Algorithm and the Genetic Algorithm, to optimize feature selection based on the given WSN dataset. After optimal feature selection and training, the machine learning classification algorithms classify whether the traffic is normal or benign in the form of four types of DoS attacks, namely, Blackhole, Scheduling, Flooding, and Grayhole attacks. The proposed model and algorithms used are further validated and compared based on standard performance metrics. The experiments conducted during the research show that the GOA-GA method, when combined with the KNN classifier, achieves an accuracy of 95.51% and a recall of 95.51%, exhibiting competitive performance relative to recent state-of-the-art approaches while reducing feature dimensionality and computational overhead. These results indicate that the proposed hybrid optimization strategy offers a robust and efficient solution for DoS attack detection in WSNs, contributing to ongoing research in information security.