Abstract:
Intrusion Detection System (IDS) is becoming a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks ...Show MoreMetadata
Abstract:
Intrusion Detection System (IDS) is becoming a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks in an interconnected network thereby securing the network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. This paper focuses on a hybrid approach for intrusion detection system (IDS) based on data mining techniques. The main research method is clustering analysis with the aim to improve the detection rate and decrease the false alarm rate. Most of the previously proposed methods suffer from the drawback of k-means method with low detection rate and high false alarm rate. This paper presents a hybrid data mining approach encompassing feature selection, filtering, clustering, divide and merge and clustering ensemble. A method for calculating the number of the cluster centroid and choosing the appropriate initial cluster centroid is proposed in this paper. The IDS with clustering ensemble is introduced for the effective identification of attacks to achieve high accuracy and detection rate as well as low false alarm rate.
Published in: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Date of Conference: 22-25 August 2013
Date Added to IEEE Xplore: 21 October 2013
ISBN Information:
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- IEEE Keywords
- Index Terms
- Data Mining ,
- Intrusion Detection ,
- Intrusion Detection Approaches ,
- High Detection ,
- False Alarm Rate ,
- Higher Detection Rate ,
- Cluster Centroids ,
- World Today ,
- Low Detection Rate ,
- High Accuracy Rate ,
- Intrusion Detection System ,
- Kinds Of Attacks ,
- High False Alarm Rate ,
- High False Alarm ,
- Low False Alarm ,
- Low False Alarm Rate ,
- Feature Merging ,
- Ensemble Clustering ,
- Clustering Algorithm ,
- Clustering Method ,
- Anomaly Detection ,
- Sum Of Distances ,
- Means Algorithm ,
- Advantages Of The Proposed Method ,
- Unsupervised Learning ,
- Means Clustering ,
- Naive Bayes ,
- Cluster Formation ,
- K-nearest Neighbor ,
- Attack Patterns
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Data Mining ,
- Intrusion Detection ,
- Intrusion Detection Approaches ,
- High Detection ,
- False Alarm Rate ,
- Higher Detection Rate ,
- Cluster Centroids ,
- World Today ,
- Low Detection Rate ,
- High Accuracy Rate ,
- Intrusion Detection System ,
- Kinds Of Attacks ,
- High False Alarm Rate ,
- High False Alarm ,
- Low False Alarm ,
- Low False Alarm Rate ,
- Feature Merging ,
- Ensemble Clustering ,
- Clustering Algorithm ,
- Clustering Method ,
- Anomaly Detection ,
- Sum Of Distances ,
- Means Algorithm ,
- Advantages Of The Proposed Method ,
- Unsupervised Learning ,
- Means Clustering ,
- Naive Bayes ,
- Cluster Formation ,
- K-nearest Neighbor ,
- Attack Patterns
- Author Keywords