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@article{166391,
        author = {Harini Induri and Dr. M . Dhanalakshmi},
        title = {NIDS-HCTWSVM:A Network Intrusion Detection System Based on Hierarchical Clustering and Twin Support Vector Machine},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {793-801},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166391},
        abstract = {Technology is upgrading day by day. With the improving technology, the security issues are also becoming challenging. As the number of people using network are increasing, the data will also get increases. It’s necessary to know if any person is entering our network which can give the attacker a chance to steal and misuse data. To handle this kind of issues, having an intrusion detection system is really necessary which can detect and give output of network traffic if it’s normal or not. This study first proposes a solution for designing network intrusion detection system by combining hierarchical clustering, Decision tree, Random Forest, and, twin support vector machine, that can be effectively used to detect various classes of network intrusion. Apart from that, different ML models like SVM, Random Forest, gradient boosting and so on are also trained to check the efficiency of the proposed system. The designed system will detect if the network traffic is Normal or not. If it’s an attack it also specifies the type of attack. In Combined model, hierarchical clustering algorithm will be applied for making network traffic uniform for training. After implementing the agglomerative clustering, a decision tree is constructed to reach the goal of reducing the error accumulation while constructing the decision tree. Finally, based on the above results, a network intrusion detection model is realized by incorporating twin support vector machines in the decision tree built, which detects the category of network intrusion.},
        keywords = {Decision Tree, Gaussian Naïve Bayes, Gradient boosting, Hierarchical clustering, IDS, Random Forest, SVM, Twin SVM.},
        month = {July},
        }