Extending Network Intrusion Detection with Enhanced Particle Swarm Optimization Techniques
<|reference_start|>Extending Network Intrusion Detection with Enhanced Particle Swarm Optimization Techniques: 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.<|reference_end|>
@article{songma2024extending,
    title={Extending Network Intrusion Detection with Enhanced Particle Swarm
  Optimization Techniques},
    author={Surasit Songma, Watcharakorn Netharn and Siriluck Lorpunmanee},
    journal={arXiv preprint arXiv:2408.07729},
    year={2024},
    doi={10.5121/ijcnc.2024.16404},
    archivePrefix={arXiv},
    eprint={2408.07729},
    primaryClass={cs.CR}
}