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@article{183437,
        author = {Sonam Jayaswal and Dr. Gholam Mursalin Ansari and Dr. Upendra Kumar},
        title = {Analysis of Intrusion Detection using Machine Learning for Computer Network System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2011-2021},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183437},
        abstract = {In today’s generation internet has become a very essential component for almost everything. According to an estimate it is suggested that at least 2.5 quintillion bytes of data is generated by a human being on a daily basis. This large dumps of data increases the risk of network attacks to an alarming rate jeopardizing the integrity and confidentiality of the users. These penetration in the network is increasing day by day and becoming more sophisticated and complexed. This is where the IDS (Intrusion detection system) comes into picture, providing a protecting layer over the infrastructure with its continuous adaptation. Through this paper we tested various machine learning classifiers on KDD and UNSW-NB15 dataset to strengthen the detection ratio in IDS. The main focus was on the false negative and false positive performance matrix for more accurate detection in the intrusion detection system. Lastly, we tried to test the classifiers with the important features of the dataset along with the use of ensemble methods to enhance the performance of the IDS.},
        keywords = {Intrusion detection, KDD dataset, UNSW-NB15, SVM, Random forest, F1 Score, Accuracy.},
        month = {August},
        }