Abstract
Network security is extremely important and mission-critical not only for business continuity but also for thousands of other huge and increasing number of systems and applications running over network continuously to deliver services. Intrusion-detection systems were emerged as a best way to improve network security. Traditional intrusion detection systems are rule-based and are not effective in detecting new and previously unknown intrusion events. Data mining techniques and machine algorithms have recently gained attention as an alternative approach to proactively detect network security breaches. In this research work, experimentations are performed on NSL-KDD dataset using various data mining algorithms. Decision Tree, Naïve Bayes, Random Forest, and Logistic Regression classifiers were used. The results obtained showed that models are biased towards classes with low distribution in the dataset.
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Gururaja, H.S., Seetha, M. (2022). Analysis of an Ensemble Model for Network Intrusion Detection. In: Mohanty, M.N., Das, S., Ray, M., Patra, B. (eds) Meta Heuristic Techniques in Software Engineering and Its Applications. METASOFT 2022. Artificial Intelligence-Enhanced Software and Systems Engineering, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-11713-8_32
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DOI: https://doi.org/10.1007/978-3-031-11713-8_32
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