Leveraging Metaheuristics for Feature Selection With Machine Learning Classification for Malicious Packet Detection in Computer Networks

Robust Intrusion Detection Systems (IDS) are increasingly necessary in the age of big data due to the growing volume, velocity, and variety of data generated by modern networks. Metaheuristic algorithms offer a promising approach to enhance IDS performance in terms of optimal feature selection. Combining these algorithms along with Machine learning (ML) for the creation of an IDS makes it possible to improve detection accuracy, reduce false positives and negatives, and enhance the efficiency of network monitoring. Our study proposes using metaheuristic algorithms along with machine learning classifiers for feature selection to optimize the number of features from the data set of computer network traffic. We have tested several combinations of algorithms viz., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) along with ML algorithms viz., Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB) and Logistic Regression (LR). The combinations of algorithms have been tested over the NSS-KDD and kddcupdata_10% data sets. We have drawn several insights on feature selection scores with respect to test scores, FI scores, recall and precision for various algorithm combinations. The feature selection time has also been highlighted to showcase the fastest-performing algorithm combinations. Ultimately, we have presented three combinations of algorithms depending on organizational IDS requirements and provided separate solutions for each.