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@article{192181,
        author = {Raksha K and Guruprasanna J K},
        title = {An Efficient Machine Learning–Based Intrusion Detection system for Detecting Modern Network Attacks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {489-492},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192181},
        abstract = {Intrusion detection systems (IDS) based on signatures and predefined rules are not enough anymore to ensure strong network security because of the quick growth of networked systems and the growing sophistication of cyberattacks. Because methods for machine learning will identify known and new intrusions and understand intricate attack patterns, they have emerged as a potential substitute. However, current ML based- tools that look for intrusions and are based on drawbacks, including dependency on out-of-date datasets, inadequate real-time performance evaluation, and poor handling of skewed data in this regard to identify current network risks in modern network environments, this study represents an effective ML-oriented intrusion detection system to get better identifiable accuracy and reduce false-positive rates, the suggested method combines feature selection, data preprocessing, and many machine learning classifiers. Benchmark intrusion detection datasets they are utilized in the experiments, and identifiable accuracy, precision, recall, and ROC-AUC are utilized to make the evaluation performance of different models. The outcomes indicate that the given framework outperforms conventional methods in the way of detection, underscoring its efficacy and applicability for real-world implementation in contemporary network security systems.},
        keywords = {Intrusion Detection System, Machine Learning, Network Security, Cyber Attacks, Anomaly Detection},
        month = {February},
        }