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• W3043433852 abstract "The ever-growing security challenges have been a hindrance to the success of Information Technology Innovations due to multifaceted network intrusions. Hence, it becomes imperative to provide tools that can address without compromising integrity, confidentiality and availability of network resources. This paper presents a model for detecting intrusion in a network using Negative Selection Algorithm. Negative Selection which is Human Immune System (HIS) inspired has been used for anomaly detection due to its self-non-self-discrimination potential. However, it suffers from high rate of false positives and scalability issues. This paper addresses the issues using feature selection to reduce the dimensionality of the dataset. The intrusion detection model is evaluated using NSL-KDD dataset. The results obtained using the benchmark dataset showed that the scalability issue reduced in the proposed approach." @default.
• W3043433852 created "2020-07-23" @default.
• W3043433852 creator A5007301128 @default.
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• W3043433852 date "2020-06-01" @default.
• W3043433852 modified "2023-09-27" @default.
• W3043433852 title "Negative Selection Algorithm Based Intrusion Detection Model" @default.
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• W3043433852 doi "https://doi.org/10.1109/melecon48756.2020.9140562" @default.
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