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@article{185093,
        author = {Dr. Gholam Mursalin Ansari and Sonam Jayaswal and Dr. Upendra Kumar},
        title = {A Comprehensive Approach for Detection of Intrusion in Computer Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {478-488},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185093},
        abstract = {Now a day’s Network Intrusion is the universal issue for users. One of the strategies view to prevent intrusion is to develop an Intrusion Detection System (IDS). Moreover, attackers frequently modify their tools and methodologies to change their attacking patterns, therefore putting a recognized IDS system in one place is generally difficult. Recently network environments getting more complex and having more hosts are becoming vulnerable to attack. The goal of this research is to analyze existing models which is the best suitable model to detect intrusion in minimum time. Although it's crucial to talk about methodical, effective, and automated intrusion detection models. In this research work, we explore machine learning strategies for Intrusion Detection on the UNSW-NB15 dataset. Although we are analyzing and implementing several model to find better output. The goal is to increase the intrusion detection rate by focusing on false positive and false negative performance indicators. The results of this research indicates that the Random Forest classifier will the best average accuracy rate Although the XG Boost classifier will the lowest value for false negatives. Further we also analyze time complexity of the various machine learning models.},
        keywords = {IDS, SVM. Random   Forest, KNN, Bagging, Boosting, Network Intrusion},
        month = {October},
        }