Abstract
Aiming at the intrusion detection problem of the wireless sensor network (WSN), considering the combined characteristics of the wireless sensor network, we consider setting up a corresponding intrusion detection system on the edge side through edge computing. An intrusion detection system (IDS), as a proactive network security protection technology, provides an effective defense system for the WSN. In this paper, we propose a WSN intelligent intrusion detection model, through the introduction of the k-Nearest Neighbor algorithm (kNN) in machine learning and the introduction of the arithmetic optimization algorithm (AOA) in evolutionary calculation, to form an edge intelligence framework that specifically performs the intrusion detection when the WSN encounters a DoS attack. In order to enhance the accuracy of the model, we use a parallel strategy to enhance the communication between the populations and use the Lévy flight strategy to adjust the optimization. The proposed PL-AOA algorithm performs well in the benchmark function test and effectively guarantees the improvement of the kNN classifier. We use Matlab2018b to conduct simulation experiments based on the WSN-DS data set and our model achieves 99% ACC, with a nearly 10% improvement compared with the original kNN when performing DoS intrusion detection. The experimental results show that the proposed intrusion detection model has good effects and practical application significance.
Highlights
We propose an intelligent intrusion detection model based on edge intelligence that is deployed at the edge of the wireless sensor network (WSN) node; We propose a parallelized arithmetic optimization algorithm and achieve outstanding results compared to another algorithm; Through standard data set testing, our edge intelligent intrusion detection model has good performance in detecting DoS attacks
In order to verify the practicability of the intrusion detection model proposed in this paper, the WSN intrusion detection data set WSN-DS [48] was used in simulation experiments
The total population of the three evolutionary algorithms of PSO, Arithmetic Optimization Algorithm (AOA), and Parallel Lévy AOA (PL-AOA) was set to 20, and the number of iterations was 100.We can clearly find the model kNNPL−AOA achieved the best results on the three indicators of accuracy rate (ACC), detection rate (DR), and false positive rate (FPR)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The k-Nearest Neighbor (kNN) method is a normal machine learning method, whose structure is simple and easy to implement, and the classification effect is good Because it eliminates the training process of neural networks, it is often used as a lightweight machine learning method in the intrusion detection systems of traditional networks. The algorithm has few control parameters, a simple structure, ease of implementation, and has an excellent performance in a variety of industrial optimization problems In this regard, this article will focus on using the AOA algorithm to optimize the kNN weight and k value selection. We propose an intelligent intrusion detection model based on edge intelligence that is deployed at the edge of the WSN node (kNNPL−AOA ); We propose a parallelized arithmetic optimization algorithm and achieve outstanding results compared to another algorithm; Through standard data set testing, our edge intelligent intrusion detection model has good performance in detecting DoS attacks.
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