Intrusion detection systems for wireless sensor networks using computational intelligence techniques

Abstract

Network Intrusion Detection Systems (NIDS) are utilized to find hostile network connections. This can be accomplished by looking at traffic network activity, but it takes a lot of work. The NIDS heavily utilizes approaches for data extraction and machine learning to find anomalies. In terms of feature selection, NIDS is far more effective. This is accurate since anomaly identification uses a number of time-consuming features. Because of this, the feature selection method influences how long it takes to analyze movement patterns and how clear it is. The goal of the study is to provide NIDS with an attribute selection approach. PSO has been used for that purpose. The Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network, allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being compromised. In the research, datasets were produced utilising both a network infrastructure and a simulation network. Wireshark is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated environment. Additionally, a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot, has been built and communication packets are being recorded using the Wireshark tool. To train several machine learning models, all the datasets that were gathered—created datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle—were employed. Additionally, PSO, which is an optimization method, has been used with these ML algorithms for feature selection. In the research, KNN, decision trees, and ANN have all been combined with PSO for a specific case study. And it was found demonstrated the classification methods PSO + ANN outperformed PSO + KNN and PSO + DT in this case study.

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“…The system involves trained and evaluated forecast models within the classifiers. Sivagaminathan V et al 20 aimed to improve NADS using PSO combining with Artificial Neural Network and K-Nearest Neighbor (PSO+ANN) and (PSO+KNN). Data was collected using Wireshark, and a simulated network was created using Cisco Packet…”

“…Sivagaminathan et al 20 Proposed PSO combined with ANN and KNN for NADS improvement-Data collection using Wireshark and simulated network creation.…”

“…where, a random angle between 0 and 2𝜋 is denoted as 𝜑 rand , and the finest final solution attained by the present solution is indicated by ⃗ i m I . However, if the probability z is surpassed by f, the newly updated solution is restructured by following Equation (20).…”

“…Several existing approaches are considered for comparison, including EPK-DNN, 16 IMOPSO, 17 AIDS-HML, 19 PSO+KNN, 20 PSO+ANN, 20 FSACM, 21 LightGBM-DAE, 22 HTF-DS, 24 DHMLM, 25 SGM, 26 and HDL 27 using NADS datasets.…”