Extending Network Intrusion Detection with Enhanced Particle Swarm Optimization Techniques

TL;DR

This paper enhances Network Intrusion Detection Systems by integrating Enhanced Particle Swarm Optimization with machine learning models, significantly improving detection accuracy and reliability against cybersecurity threats.

Contribution

It introduces EPSO to optimize ML classifiers, notably Decision Trees, for more effective and dependable intrusion detection in cybersecurity.

Findings

Decision Tree with EPSO outperforms other models in accuracy.

EPSO improves model precision, recall, and F1-score.

Framework demonstrates high reliability in detecting network breaches.

Abstract

The present research investigates how to improve Network Intrusion Detection Systems (NIDS) by combining Machine Learning (ML) and Deep Learning (DL) techniques, addressing the growing challenge of cybersecurity threats. A thorough process for data preparation, comprising activities like cleaning, normalization, and segmentation into training and testing sets, lays the framework for model training and evaluation. The study uses the CSE-CIC-IDS 2018 and LITNET-2020 datasets to compare ML methods (Decision Trees, Random Forest, XGBoost) and DL models (CNNs, RNNs, DNNs, MLP) against key performance metrics (Accuracy, Precision, Recall, and F1-Score). The Decision Tree model performed better across all measures after being fine-tuned with Enhanced Particle Swarm Optimization (EPSO), demonstrating the model's ability to detect network breaches effectively. The findings highlight EPSO's importance in improving ML classifiers for cybersecurity, proposing a strong framework for NIDS with high precision and dependability. This extensive analysis not only contributes to the cybersecurity arena by providing a road to robust intrusion detection solutions, but it also proposes future approaches for improving ML models to combat the changing landscape of network threats.