Abstract
Intrusion detection concept plays a vital role in personal computer (PC) security design. Intrusion detection system (IDS) essentially fits in as a unit which monitors the user activities, incoming traffic, and then distinguishes or classifies which one is intrusion and which one is normal or legitimate. Fundamentally IDS recognizes any misuse or unauthorized access which is essentially an attack to the crucial assets of the system or network. It detects the malicious traffic data on a network or a host. The task of desired classification often gets affected by the presence of noisy, redundant, and irrelevant data input. Occurrence of noise in dataset leads to poor classification as it increases the possibility of wrong detection of a class. High misclassification rate and low detection rate by the classifier in IDS enlarges feature space. To overcome this limitation, hybrid intrusion system (H-IDS) is proposed in this paper. H-IDS uses a hybrid strategy with support vector machine (SVM) and intelligent water drops (IWD) to execute the feature selection and classification techniques for IDS. Experimentations reveal that proposed H-IDS helps to accomplish the goal by attaining high classification, detection, and precision as compared to current state of the art.
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Hariyale, N., Rathore, M.S., Prasad, R., Saurabh, P. (2020). A Hybrid Approach for Intrusion Detection System. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_31
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DOI: https://doi.org/10.1007/978-981-15-0035-0_31
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