A novel hybrid intrusion detection method integrating anomaly detection with misuse detection

Kim, Gisung; Lee, Seungmin; Kim, Sehun

doi:10.1016/j.eswa.2013.08.066

Cited by 417 publications

(186 citation statements)

References 11 publications

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“…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. Because of the cluster boundary point forward neighborhood and negative half neighborhood point number differs greatly, two minutes boundary is high, and the clustering internal points/noise isolation of plus or minus half the number of neighborhood points within the difference is not big, small border degree, so the algorithm can contain data sets general boundary point and noise point/distinguish isolated point [1].…”

Section: Boundary Point Detection Algorithm Based On Densitymentioning

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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Section: Boundary Point Detection Algorithm Based On Densitymentioning

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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“…Kim et al [4] proposed a new hybrid intrusion detection model that integrates both misuse and anomaly detection model hierarchically. They decomposed the normal data into smaller subsets using misuse detection model.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms

Haque¹,

Al-Kharobi²

2015

IJMLC

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Abstract-In this paper, we propose a novel method using ensemble learning scheme for classifying network intrusion detection from the most renowned KDD cup dataset. We have shown that reducing the dimensionality of the large dataset provides most accurate detection. Additionally, several machine learning algorithms are used to generate the accuracy metrics and analyzed further for proper comparison. Our approach found out that this algorithm outperforms all other learning techniques. Our goal is to analyze the network intrusion data and find out the best components and use them for the attack analysis. This scheme can be used in parallel with the intrusion detection system to augment its prediction performance for the future data packets. Empirical results show that the input dimensionality reduction can provide lightweight intrusion detection system that can be embedded with the vulnerable system for generating correct classification with significance improvement in execution time.

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“…For example, through the network layer of the IP package, the link layer data frame, etc. Data preprocessing is mainly to collect the network data pretreatment to make it standardized, it is transformed into a suitable data format, and the data to feature selection, filtering noise, filtering and other operations [1].The project team uses the classic KDD Cup 1999 data set to do validation experiments.…”

Section: Data Processingmentioning

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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A novel hybrid intrusion detection method integrating anomaly detection with misuse detection

Cited by 417 publications

References 11 publications

Application of clustering analysis in Intrusion Detection

Application of clustering analysis in Intrusion Detection

Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms

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

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