Abstract
A good feature selection policy which can choose significant and as less as possible features plays a key role for any successful NIDS. The paper presents a genetic algorithm combined with kNN (k-Nearest Neighbor) for feature weighting. We weight all initial 35 features in the training phase and then select tops of them to implement a NIDS for testing. Many DoS/DDoS attacks are applied to evaluate the system. For known attacks we can get the best 97.42% overall accuracy rate while only the top 19 features are considered; as for unknown attacks, we can get the best 78% overall accuracy rate by top 28 features.
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Su, MY., Chang, KC., Wei, HF., Lin, CY. (2008). Feature Weighting and Selection for a Real-Time Network Intrusion Detection System Based on GA with KNN. In: Yang, C.C., et al. Intelligence and Security Informatics. ISI 2008. Lecture Notes in Computer Science, vol 5075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69304-8_20
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DOI: https://doi.org/10.1007/978-3-540-69304-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69136-5
Online ISBN: 978-3-540-69304-8
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