An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection

Lin, Shih-Wei; Ying, Kuo‐Ching; Lee, Chou‐Yuan; Lee, Zne-Jung

doi:10.1016/j.asoc.2012.05.004

Cited by 199 publications

(92 citation statements)

References 24 publications

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“…If it is, then continue to judge the neighbor data points, until the exception cannot find neighbors so far; if not, then marked as normal behavior. The intrusion behavior, which is judged by the artificial judgment stage, is unknown [4].…”

Section: Intrusion Judgmentmentioning

confidence: 99%

Application of hospital information security intrusion detection system based on the new NPRIM algorithm

Li¹,

Xu²

2016

Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials

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Abstract. The specific model of intrusion detection based on clustering and boundary points detection was elaborateed in this paper, and the data processing, clustering analysis, intrusion judgment, intrusion response, typical data warehousing the five stages are described in detail; Then the experimental environment and the experimental results were compared and analyzed, and further validation based on the improved NPRIM algorithm of the project team applied to intrusion detection is effective and feasible.

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Section: Intrusion Judgmentmentioning

confidence: 99%

Application of hospital information security intrusion detection system based on the new NPRIM algorithm

Li¹,

Xu²

2016

Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials

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“…p q Figure 1 Characteristics of boundary points FRINGE algorithm uses the grid technology and the characteristics of the angle, is based on the angle of the boundary point detection algorithm in a better detection algorithm. The algorithm like GRIDEN algorithm with the help of some of the concept of grid, such as data space, grid cell, the density of grid, grid, neighbors, dense grids and sparse grid, etc [4].…”

Section: Edge Detection Algorithm Based On Anglementioning

confidence: 99%

Application of clustering analysis in Intrusion Detection

Zhang¹,

Li²,

Xu³

2017

The 3rd International Conference on Intelligent Energy and Power Systems (IEPS 2017)

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Clustering technology and boundary point detection technology and its application in intrusion detection system are introduced in this paper from three aspects, which are the application of clustering analysis, boundary detection and clustering analysis in Intrusion Detection System. The data processing and the requirement of clustering algorithm for intrusion detection system are introduced in detail. Analyzed the result of the experiment environment and experiment, further validation of this project is based on the improved NPRIM algorithm applied to intrusion detection is effective and feasible. Boundary point detection technologyThe cluster boundary point is a point that has two or more clustering characteristics between the cluster and the cluster. The study of clustering boundary points is an important branch of clustering analysis, which plays an important role in disease prevention, biology, image retrieval, virtual reality, and improving clustering accuracy. Since Chenyi Xia first proposed the boundary point detection algorithm (BORDER) in 2006, researchers have proposed some boundary detection algorithms. In order to describe these algorithms, the algorithm is divided into four categories: density based boundary detection algorithm, grid based boundary detection algorithm and angle based boundary detection algorithm. Boundary point detection algorithm based on densityBased on the density of the boundary point detection algorithm is the use of clustering near the boundary of the uneven distribution of data objects to extract the characteristics of the clustering boundary point. On the noisy data set, the algorithm can separate the boundary point from the noise region, especially the uniform data set. BRIM is a typical boundary detection algorithm in this algorithm.In order to solve the existing problems of BORDER algorithm, BRIM is a density based boundary point detection algorithm, which can effectively detect the boundary of clustering in noisy data sets. The algorithm first according to the data object plus or minus the number of data points difference within half a neighborhood to calculating the boundary of points, and then the boundary is greater than the boundary degrees threshold  marked point boundary point.

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“…[12] and [13] as well. [14] proposed an algorithm to use SVM and simulated annealing to find the best selected features to improve the accuracy of anomaly intrusion detection. [15] reported mutual information-based feature selection method results in detecting intrusions with higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

Feature Grouping for Intrusion Detection System Based on Hierarchical Clustering

Song

Zhu

Price

2014

Advanced Information Systems Engineering

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Part 2: 4th International Workshop on Security and Cognitive Informatics for Homeland Defense (SeCIHD 2014)International audienceIntrusion detection is very important to solve an increasing number of security threats. With new types of attack appearing continually, traditional approaches for detecting hazardous contents are facing a severe challenge. In this work, a new feature grouping method is proposed to select features for intrusion detection. The method is based on agglomerative hierarchical clustering method and is tested against KDD CUP 99 dataset. Agglomerative hierarchical clustering method is used to construct a hierarchical tree and it is combined with mutual information theory. Groups are created from the hierarchical tree by a given number. The largest mutual information between each feature and a class label within a certain group is then selected. The performance evaluation results show that better classification performance can be attained from such selected features

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An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection

Cited by 199 publications

References 24 publications

Application of hospital information security intrusion detection system based on the new NPRIM algorithm

Application of hospital information security intrusion detection system based on the new NPRIM algorithm

Application of clustering analysis in Intrusion Detection

Feature Grouping for Intrusion Detection System Based on Hierarchical Clustering

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