EXTENDING NETWORK INTRUSION DETECTION WITH ENHANCED PARTICLE SWARM OPTIMIZATION TECHNIQUES

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dc.contributor.authorSurasit Songma
dc.contributor.authorWatcharakorn Netharn
dc.contributor.authorSiriluck Lorpunmanee
dc.date.accessioned2025-03-10T07:34:21Z
dc.date.available2025-03-10T07:34:21Z
dc.date.issued2024
dc.description.abstractThe 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. © (2024), (Academy and Industry Research Collaboration Center (AIRCC)). All Rights Reserved.
dc.identifier.citationInternational Journal of Computer Networks and Communications
dc.identifier.doi10.5121/ijcnc.2024.16404
dc.identifier.issn9752293
dc.identifier.scopus2-s2.0-85200383073
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4492
dc.languageEnglish
dc.publisherAcademy and Industry Research Collaboration Center (AIRCC)
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectCSE-CIC-IDS 2018
dc.subjectDeep Learning
dc.subjectIntrusion Detection System
dc.subjectLITNET-2020
dc.subjectMachine Learning Techniques
dc.subjectParticle Swarm Optimization
dc.titleEXTENDING NETWORK INTRUSION DETECTION WITH ENHANCED PARTICLE SWARM OPTIMIZATION TECHNIQUES
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85200383073&doi=10.5121%2fijcnc.2024.16404&partnerID=40&md5=0d3a8054fa52b30aa26c00acfbeb2708
oaire.citation.endPage85
oaire.citation.issue4
oaire.citation.startPage61
oaire.citation.volume16
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