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In the recent years, the improvement in the security is a challenging task in the Internet environment The Intrusion Detection System (IDS) is one of the significant tools used to detect the attacks. Various IDS techniques have been proposed to identify the attacks and alert the user or administrator about the attacks. However, they are unable to manage new attacks. This paper proposes an Intrusion Detection System based on the density maximization-based fuzzy c-means clustering (DM-FCC). In this approach, cluster efficiency is improved through a membership matrix generation (MMG) algorithm. Dissimilarity Distance Function (DDF) has been used to compute the distance metric while creating a cluster in proposing an IDS. The proposed enhanced fuzzy c-means algorithm has been tested upon ADFA Dataset and the model performs highly appreciable in terms of accuracy, precision, detection rates, and false alarms.
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- An Enhanced Approach to Fuzzy C-means Clustering for Anomaly Detection
- Springer Singapore
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