Abstract
The aim of intrusion detection systems (IDSs) is to detect the malicious traffic and dynamic traffic which changes according to network characteristics, so intrusion detection system should be adaptive in nature. Many of IDS have been developed based on machine learning approaches. In proposed approach, experiment have been carried out on KDD-99 dataset with three classes DoS attack, other attacks and normal (without any attack). Paper checks the potential capability of optimization-based features with artificial neural network (ANN) classifier for the different types of intrusion attacks. A comparative analysis with ANN and other optimizer with ANN has been carried out. The experimental results show that the accuracy of intrusion detection using particle swarm optimization with genetic algorithm (PSO_GA) improves the results significantly by reducing false positive alarms and also improve individual class detection.
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Sachin, Rama Krishna, C. (2019). A Hybrid Approach to Mitigate False Positive Alarms in Intrusion Detection System. In: Smys, S., Bestak, R., Chen, JZ., Kotuliak, I. (eds) International Conference on Computer Networks and Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 15. Springer, Singapore. https://doi.org/10.1007/978-981-10-8681-6_77
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DOI: https://doi.org/10.1007/978-981-10-8681-6_77
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