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@article{185318,
        author = {Renjith M},
        title = {AI-based Network Intruder Security Systems Incorporate Machine Learning (ML)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {861-867},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185318},
        abstract = {This paper presents the development and evaluation of AI-based network intruder security systems leveraging machine learning (ML) techniques to enhance cyber defense. The approach integrates both signature-based and anomaly-based detection methods, enabling the identification of known and unknown network threats. Multiple ML and deep learning models are trained and compared using diverse datasets to assess their effectiveness in accurately detecting intrusions while reducing false positives. Experimental results indicate that deep learning architectures, particularly those employing ensemble strategies, outperform traditional ML models in terms of detection accuracy and robustness. The study highlights the strengths and challenges of implementing ML within AI-powered NIDS, including computational complexity and dataset relevance, and outlines future research avenues aimed at creating lightweight yet efficient frameworks for real-time threat detection. By demonstrating advanced capabilities for automated and adaptive network security, this work contributes valuable insights toward fortifying organizations against evolving cyber threats.},
        keywords = {Network Intrusion Detection, Artificial Intelligence, Machine Learning, Deep Learning, Anomaly Detection, Signature-based Detection, Intrusion Detection System (IDS), Cybersecurity, Neural Networks, Data Preprocessing, Feature Selection, Supervised Learning, Unsupervised Learning, Ensemble Methods, False Positive Reduction, Real-time Monitoring, Network Security, Threat Detection, Model Evaluation, Automated Intrusion Prevention.},
        month = {October},
        }