A graph-based clustering algorithm for anomaly intrusion detection

Mingqiang, Zhou; Huang, Hui; Wang, Qian

doi:10.1109/iccse.2012.6295306

Cited by 38 publications

(21 citation statements)

References 8 publications

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“…[3] the paper also discussed the protocols and their immunities to different attacks with analytical modeling and mathematical formulation. A graph based approach is proposed by Zhou Mingqiang et al [11] they proposed graph-based intrusion detection algorithm by using outlier detection method that based on local deviation coefficient (LDCGB). Compared to other intrusion detection algorithm of clustering, this algorithm is unnecessary to initial cluster number.…”

Section: Releted Workmentioning

confidence: 99%

Intrusion Detection System based on Fuzzy C Means Clusteringand Probabilistic Neural Network

Kulhare¹,

Singh²

2013

IJCA

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Security is always an important issue especially in the case of computer network which is used to transfer personal/confidential information's, ecommerce and media sharing. Since the network is closely related to operating its conditions hence a careful observation & analysis of network characteristics could describe the state of the network such as network is under specific attack or operating normally. This paper presents an intrusion detection system based on fuzzy C-means clustering and probabilistic neural network which not only reduces the training time but also increases the detection accuracy. The proposed system is tested using KDD99 dataset and the simulation results shows that by selecting effective characteristics and proper training the detection accuracy rate up to 99% is achievable.

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Section: Releted Workmentioning

confidence: 99%

Intrusion Detection System based on Fuzzy C Means Clusteringand Probabilistic Neural Network

Kulhare¹,

Singh²

2013

IJCA

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“…One example of anomaly-based implementations is the use of data mining algorithms to extract and identify valid, novel, and useful patterns in audit data. Several data mining algorithms have been used for anomaly-based IDPS including support vector machines [67,169], genetic algorithms [66,186], neural networks [211,278], and clustering [135,207,212], all of which improve the detection accuracy of IDPS and assist in detecting new attacks.…”

Section: Detection Methodologiesmentioning

confidence: 99%

Security configuration management in intrusion detection and prevention systems

Alsubhi

Alhazmi

Bouabdallah

et al. 2012

IJSN

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Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defense against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. IDPSs can be network or host-based and can collaborate in order to provide better detection of malicious traffic. Although several IDPS systems have been proposed, their appropriate configuration and control for effective detection/prevention of attacks and efficient resource consumption is still far from trivial.Another concern is related to the slowing down of system performance when maximum security is applied, hence the need to trade off between security enforcement levels and the performance and usability of an enterprise information system.In this dissertation, we present a security management framework for the configuration and control of the security enforcement mechanisms of an enterprise information system. The approach leverages the dynamic adaptation of security measures based on the assessment of system vulnerability and threat prediction, and provides several levels of attack containment. Furthermore, we study the impact of security enforcement levels on the performance and usability of an enterprise information system. In particular, we analyze the impact of an IDPS configuration on the resulting security of the network, and on the network performance. We also analyze the performance of the IDPS for different configurations and under different traffic characteristics. The analysis can then be used to predict the impact of a given security configuration on the prediction of the impact on network performance.iii Acknowledgements All praise is due to Allah who guided me through out this research and beyond.My deepest gratitude is to my advisor, Prof. Raouf Boutaba, for his guidance, support, patience, and encouragement. I have been amazingly fortunate to have an advisor who gave me the freedom to explore on my own, and at the same time the guidance to recover when my steps faltered. He was always available, professional, and friendly. I have learned from him to think differently, be optimistic, and be self motivated. This work would have not been done without his advice and valuable comments.

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“…Various data mining's methods and algorithms have been used such as classification tree and support vector machines for intrusion detection [4], Genetic Algorithms, Neural Networks [3], and Clustering [5][6][7], all these methods helps to provide a good level of security to the systems from external and internal attacks, also from new attacks.…”

Section: Introductionmentioning

confidence: 99%

Overview of intrusion detection using data-mining and the features selection

Moussaid

Toumanari

2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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Most of traditional intrusion detection systems, Anomaly-Based detection and Signature-based detection, suffer from many drawbacks. This paper exposes the limits and drawback of traditional Intrusion detection systems.Consequently the main goal of this paper is to expose data mining techniques and approaches to improve the performance of the traditional intrusion detection system to identify known and unknown attack's patterns.

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A graph-based clustering algorithm for anomaly intrusion detection

Cited by 38 publications

References 8 publications

Intrusion Detection System based on Fuzzy C Means Clusteringand Probabilistic Neural Network

Intrusion Detection System based on Fuzzy C Means Clusteringand Probabilistic Neural Network

Security configuration management in intrusion detection and prevention systems

Overview of intrusion detection using data-mining and the features selection

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