Machine Learning-Based Intrusion Detection Systems for Next-Generation Networks
DOI:
https://doi.org/10.15662/IJARCST.2019.0205002Keywords:
Machine Learning, Intrusion Detection System (IDS), Next-Generation Networks (NGNs), Anomaly Detection, Network Security, Supervised Learning, Unsupervised Learning, Deep Learning, CybersecurityAbstract
The rapid evolution of next-generation networks (NGNs), characterized by heterogeneous architectures, high-speed data transmission, and increased connectivity, has escalated the challenges in network security. Traditional intrusion detection systems (IDS) struggle to keep pace with the dynamic and complex nature of modern cyber threats. Machine learning (ML) techniques have emerged as promising solutions, offering adaptive, automated, and intelligent detection capabilities that enhance the effectiveness of IDS in NGNs. This paper reviews the state-of-the-art ML-based intrusion detection systems tailored for NGNs, highlighting key algorithms such as supervised learning (e.g., Support Vector Machines, Random Forests), unsupervised learning (e.g., clustering, anomaly detection), and deep learning models. The research methodology involves systematic literature review and comparative analysis of existing ML-IDS approaches, focusing on their detection accuracy, scalability, and response time. Key findings suggest that ML algorithms significantly improve detection rates while reducing false positives, with ensemble and hybrid models showing superior performance. The workflow of an ML-based IDS includes data collection, feature extraction, model training, validation, and real-time monitoring. Despite advantages like adaptability and automation, challenges such as data imbalance, computational complexity, and evolving attack vectors persist. The discussion underscores the need for continuous model updating and integration with existing security frameworks. The conclusion emphasizes ML's critical role in fortifying NGN security and calls for future research in federated learning, lightweight models for IoT integration, and explainable AI for transparent threat analysis. This study serves as a foundation for researchers and practitioners aiming to develop robust, intelligent IDS solutions aligned with the evolving landscape of next-generation networks.
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