Network Intrusion Detection System Based on the Combination of Multiobjective Particle Swarm Algorithm‐Based Feature Selection and Fast‐Learning Network

Abstract

Given the growth of wireless networks and the increase of the advantages and applications of communication networks, especially mobile ad hoc networks (MANETs), this type of network has attracted the attention of users and researchers more than before. The benefit of these types of networks in various kinds of networks and environments is that MANET does not require to hardware infrastructure to communicate and send and receive data packets within the network. It is one of the main reasons for using these MANET in various fields. On the other hand, the increased popularity of these networks has led to many challenges, one of the most important of which is network security. In this regard, a lack of regulatory and security infrastructure in MANETs has caused some problems in sending and receiving data, where intrusion in the network has been recognized as one of the most important issues. In MANETs, wireless notes act as a link between the source and destination nodes and play the role of relays and routers in the network. Therefore, malicious node penetration and the destruction of information packages become feasible. Today, intrusion detection systems (IDSs) are used as a solution to deal with the problem through remote monitoring of the performance and behaviors of nodes existing in wireless sensor networks. In addition to detecting malicious nodes in the network, IDSs can predict the behavior of malicious nodes in the future in most cases. Therefore, the present study introduced a network IDS (NIDS) entitled MOPSO-FLN by using a combination of multiobjective particle swarm optimization algorithm- (MOPSO-) based feature subset selection (FSS) and fast-learning network (FLN). In this work, we used the KDD Cup99 and dataset to select features, train the network, and test the model. According to the simulation results, this method was able to improve the performance of the IDS in terms of evaluation criteria, compared to other previous methods, by creating a balance between the objectives of the number of representative features and training errors based on the evolutionary power of MOPSO.

An Improved Particle Swarm Optimization Algorithm and Its Application to the Extreme Value Optimization Problem of Multivariable Function

“…e smaller the threshold n of 10∼100-dimensional function, the more time-consuming, but the accuracy of the solution is not necessarily high. ere is an inflection point in the solution accuracy at 5 out of n. It can be seen that the selection of n value has a certain correlation with the dimension of the function [22].…”

Intelligent monitoring of malicious intrusion behavior for power communication network channel

“…On the basis of the above, considering that the emergence of network intrusion behavior is a gradual development process, it is also carried out according to the corresponding steps, and in the actual power communication network, the diversity of intrusion behavior also makes it difficult to describe in a universal sense [23][24]. For this reason, this paper realizes the representation of network channel operation status through nodes.…”

A Novel Energy-Aware Routing in Wireless Sensor Network Using Clustering Based on Combination of Multiobjective Genetic and Cuckoo Search Algorithm

“…The PSO applies the concept of social behaviors to flocks of birds or fish to real-world problems. This approach preserves local as well as global solutions and creates the best fit for a goal [24,25]. PSO is also used to implement the information dissemination protocol in the article.…”