Intrusion Detection using a Novel Hybrid Method Incorporating an Improved KNN
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TLDR
This paper focuses on improving KNN classifier in existing intrusion detection task which combines K-MEANS clustering and KNN classification, to improve IDS performance.Abstract:
These days, with the tremendous growth of network-based service and shared information on networks, the risk of network attacks and intrusions increases too, therefore network security and protecting the network is getting more significance than before. Intrusion Detection System (IDS) is one of the solutions to detect attacks and anomalies in the network. The ever rising new intrusion or attack types causes difficulties for their detection, therefore Data mining techniques has been widely applied in network intrusion detection systems for extracting useful knowledge from large number of network data to detect intrusions. Many clustering and classification algorithms are used in IDS, therefore improving the functionality of these algorithms will improve IDS performance. This paper focuses on improving KNN classifier in existing intrusion detection task which combines K-MEANS clustering and KNN classification.read more
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References
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Journal ArticleDOI
Review: Intrusion detection system: A comprehensive review
TL;DR: Through the extensive survey and sophisticated organization, this work proposes the taxonomy to outline modern IDSs and tries to give a more elaborate image for a comprehensive review.
Journal ArticleDOI
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors
TL;DR: A novel feature representation approach, namely the cluster center and nearest neighbor (CANN) approach, which shows that the CANN classifier not only performs better than or similar to k-NN and support vector machines trained and tested by the original feature representation in terms of classification accuracy, detection rates, and false alarms.
Journal ArticleDOI
A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
TL;DR: The experimental results demonstrate that the proposed hybrid intrusion detection method is better than the conventional methods in terms of the detection rate for both unknown and known attacks while it maintains a low false positive rate.
Journal ArticleDOI
Immune system approaches to intrusion detection --- a review
TL;DR: This work provides an introduction and analysis of the key developments within the use of artificial immune systems in intrusion detection, in addition to making suggestions for future research.
Journal ArticleDOI
Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree algorithm
TL;DR: This paper proposes a method to cascade k-Means clustering and the C4.5 decision tree methods for classifying anomalous and normal activities in a computer network, and exploits the results derived from the decision tree on each cluster.
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