Integrating Network Intrusion Detection With Machine Learning Techniques for Enhanced Network Security
Year: 2025
Type: article
Abstract: In today's increasingly interconnected world, cybersecurity threats are more prevalent than ever, making robust intrusion detection systems (IDS) a critical necessity. This research introduces a hybrid IDS model that integrates Fuzzy C-Means clustering with classification methods such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Stochastic Gradient Descent (SGD), and Naïve Bayes (NB). Advanced feature selection techniques are applied to improve detection accuracy and resilience against evolving cyberattacks. Extensive experimentation is used to validate the efficacy of this approach using the NIDS dataset. This study addresses limitations in traditional IDS methods, particularly their vulnerability to novel and complex attacks, and provides insights into leveraging machine learning (ML) to strengthen network security
Institutions: Superior University, Newcastle College, Newcastle University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7217831741