Abstract
The recent advancements in the computer networks pave a sophisticated platform to the “Black hat” attackers, which poses a major challenge to network security. Intrusion detection is a significant research problem in network security which motivates the researchers to focus on the development of a robust Intrusion Detection System (IDS). Several research works in network infrastructure reveal the significance of Intrusion Detection Systems (IDS) in protecting the IT infrastructure against the ever-advancing nature of cyber attacks. In a similar manner, machine learning has a pivotal role in enhancing the performance of intrusion methodologies. Hence, this work presents Moth-Flame Optimization Algorithm-based Random Forest (MFOA-RF) to build an efficient intrusion detection methodology for networks, and it enhances the RF technique by tuning the parameter. The experiments were implemented using NSL-KDD Cup dataset and the results were validated in terms of classification accuracy and false alarm rate.
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Chaithanya, P.S., Gauthama Raman, M.R., Nivethitha, S., Seshan, K.S., Sriram, V.S. (2020). An Efficient Intrusion Detection Approach Using Enhanced Random Forest and Moth-Flame Optimization Technique. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_75
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DOI: https://doi.org/10.1007/978-981-13-9042-5_75
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