Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering

Chimphlee, Witcha; Abdullah, Abdul Hanan; Sap, Mohd. Noor Md.; Srinoy, Surat; Chimphlee, Siriporn

doi:10.1109/ichit.2006.253508

Cited by 66 publications

(35 citation statements)

References 8 publications

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“…The methods used in comparison are as follows: K-Means [28], Improved K-Means [28], K-Medoids [28], Expectation Maximization [28], Fuzzy C-Means [6], Fuzzy Rough Clustering [6]. We used accuracy and False positive rate as evaluation metrics.…”

Section: B Performance Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Anomaly based Intrusion Detection using Modified Fuzzy Clustering

Harish¹,

Kumar²

2017

IJIMAI

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Section: B Performance Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Anomaly based Intrusion Detection using Modified Fuzzy Clustering

Harish¹,

Kumar²

2017

IJIMAI

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“…Since malicious behavior is naturally different from normal behavior, abnormal behavior should be considered as outliers [59,60]. Fuzzy logic can help to construct more abstract and flexible patterns for intrusion detection and thus greatly increase the robustness and adaption ability of detection systems [58].…”

Section: Fuzzy Setmentioning

confidence: 99%

A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

2015

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In Content-Centric Networks (CCNs) as a possible future Internet, new kinds of attacks and security challenges -from Denial of Service (DoS) to privacy attacks-will arise. An efficient and effective security mechanism is required to secure content and defense against unknown and new forms of attacks and anomalies. Usually, clustering algorithms would fit the requirements for building a good anomaly detection system. K-means is a popular anomaly detection method to classify data into different categories. However, it suffers from the local convergence and sensitivity to selection of the cluster centroids. In this paper, we present a novel fuzzy anomaly detection system that works in two phases. In the first phase -the training phase-we propose an hybridization of Particle Swarm Optimization (PSO) and K-means algorithm with two simultaneous cost functions as well-separated clusters and local optimization to determine the optimal number of clusters. When the optimal placement of clusters centroids and objects are defined, it starts the second phase. In this phase -the detection phase-we employ a fuzzy approach by the combination of two distance-based methods as classification and outlier to detect anomalies in new monitoring data. Experimental results demonstrate that the proposed algorithm can achieve to the optimal number of clusters, well-separated clusters, as well as increase the high detection rate and decrease the false positive rate at the same time when compared to some other well-known clustering algorithms.

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“…Hence, outliers should be considered as abnormal behavior. Therefore, the fuzzy C-Medoids algorithm [246] and the fuzzy C-Means algorithm [52,53,54,141] are two common clustering approaches to identify outliers. As all clustering techniques, they are affected by the "curse of dimensionality", thus suffering performance degradation when confronted with datasets of high dimensionality.…”

Section: Fuzzy Anomaly Detectionmentioning

confidence: 99%

“…Feature selection is a necessary data pre-processing step. For example, Principal Component Analysis [141,246] and Rough Sets [52,53,54] can be applied on datasets before being clustered.…”

Section: Fuzzy Anomaly Detectionmentioning

confidence: 99%

The use of computational intelligence in intrusion detection systems: A review

Banzhaf

2010

Applied Soft Computing

568

248

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Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will be on core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.

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Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering

Cited by 66 publications

References 8 publications

Anomaly based Intrusion Detection using Modified Fuzzy Clustering

Anomaly based Intrusion Detection using Modified Fuzzy Clustering

A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

The use of computational intelligence in intrusion detection systems: A review

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