Enhanced Approach for Intrusion Detection System in WSN using Hybrid PSO and Stacked Classifiers

The determination of unknown attacks remains a major challenge in WSN. Network Intrusion Detection (NIDS) is a proactive network security protection technology, which provides an effective defense system for WSN. NIDS heavily utilizes approaches for data extraction and Machine Learning (ML) to find anomalies. ML is an artificial intelligence subset that refers to a set of approaches allowing to learn from a preset dataset with improvement without human intervention. In terms of feature Importance. Particle Swarm Optimization (PSO) is a method used to select features in the dataset that contribute the most to predicting the target variable. Working with selected features instead of all the features reduces the risk of over-fitting, improves accuracy and decreases the training time. PSO technique selects optimal features from the preprocessed dataset. Residue Number System (RNS) is a numeral system representing integers by their values modulo several pairwise coprime integers called the moduli. This representation is allowed by Forward Conversion, which asserts that if N is the product of the moduli, there is, in an interval of length N, exactly one integer having any given set of modular values. The goal of the study is to provide NIDS with an attribute selection approach. PSO has been used for that purpose. This proposed feature selection method integrates RNS with the advantages of both empirical mode decomposition to retain most of the relevant features. The Network Intrusion Detection model PSO-RNS is being developed to identify any malicious activity in the network or any unusual behavior in the network, allowing the identification of the illegal activities. The proposed framework validated datasets, UNSW NB-15 to train the ensemble Machine Learning classifiers, KNN, Naïve Bayes and Logistic Regression as base classifiers while Random Forest as meta classifier, all been. stacked for feature selection with PSO optimization technique. In order to enhance the accuracy of the model, RNS is used to extract features from the dataset further using moduli set of {2n - 1, 2n, 2n +1}. The proposed PSO-RNS algorithm performs well in the benchmark function test and effectively guarantees the improvement of PSO feature selection approach. Our model achieved a reduced training time with the inclusion of RNS compared with PSO for (Naïve Bayes + KNN) + Random Forest: CASE A and KNN + Logistic Regression) + Random Forest: CASE B and improved accuracy. The experimental results show that the proposed intrusion detection model has good effects and practical application significance.

Forward Conversion, Machine Learning Classifiers, Moduli set, NIDS, PSO, RNS, Stack Ensemble, UNSW NB-15, WSN

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