An Intrusion Detection System Using the Artificial Neural Network-based Approach and Firefly Algorithm

Abstract

Due to the dynamic nature and limited resources in wireless networks, attack occurrence is inevitable. These attacks can damage or weaken the transmitted packets and threaten the entire system’s efficiency. As a result, in such a situation, great and sometimes irreparable damage will be done to the business. Thus, security and attack prevention in wireless networks become a necessity and are very important. Essence intrusion detection systems determine whether a user’s performance and behavior under the control or activity of a network traffic load is malicious. Since the characteristics of user behavior and network traffic are diverse and numerous, Selecting some features is necessary to improve the classification accuracy. Therefore, in this idea, a new model for estimating the penetration of wireless network-based networks is proposed based on a combination of feature subset selection based on firewall algorithm and fast neural learning networks. In this paper, the proposed idea will use the training set from the data set collected to test intrusion detection systems called KDD Cup to determine network intrusion detection methods and evaluate the proposed model. The proposed idea, based on the results obtained from the simulation and its performance in various experiments, has shown that it has improved significantly in terms of multiple criteria such as accuracy, F-criterion rate, and efficiency compared to the neural network pattern. In other words, the proposed idea performs better than the neural network method in identifying healthy nodes and new malicious intrusions in the target network. The simulation outputs also indicate that the proposed idea has a better classification rate and F-criteria than the FLN methods based on HSO, ATLBO, GA, and PSO. Vector backup machine, multilayer perceptron network, DBN, and S-NDAE have less time.