Abstract
A large amount of work has been done on the KDD 99 dataset, most of which include the use of a hybrid anomaly and misuse detection model done in parallel with each other. In order to further classify the intrusions, our approach to network intrusion detection includes the use of two different anomaly detection models followed by misuse detection applied to the combined output obtained from the previous step. The end goal of this is to verify the anomalies detected by the anomaly detection algorithm and clarify whether they are actually intrusions or random outliers from the trained normal (and thus to try and reduce the number of false positives). We aim to detect a pattern in this novel intrusion technique itself, and not the handling of such intrusions. The intrusions were detected to a very high degree of accuracy.
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Sinha, A., Pandey, A., Aishwarya, P.S. (2021). Intrusion Detection Using a Hybrid Sequential Model. In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-5243-4_1
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DOI: https://doi.org/10.1007/978-981-15-5243-4_1
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