A Hybrid Unsupervised Clustering-Based Anomaly Detection Method
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TLDR
An unsupervised anomaly detection method is presented, which combines Sub-Space Clustering (SSC) and One Class Support Vector Machine (OCSVM) to detect attacks without any prior knowledge.About:
This article is published in Tsinghua Science & Technology.The article was published on 2021-04-05 and is currently open access. It has received 98 citations till now. The article focuses on the topics: Anomaly detection & Unsupervised learning.read more
Citations
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Cyber security
E. Sandstrom,J. Weiss +1 more
TL;DR: In this paper, the authors discuss cyber security in the electric power industry in general as well as some perspectives in VATTENFALL, one of Europe's largest electric utilities, and discuss the impact of cyber security on the VANET.
Journal ArticleDOI
Machine Learning for Anomaly Detection: A Systematic Review
TL;DR: In this article, the authors conduct a systematic literature review (SLR) which analyzes ML models that detect anomalies in their application and identify 29 distinct ML models used in the identification of anomalies.
Posted Content
A Novel Hybrid Kpca and SVM with ga Model for Intrusion Detection
Abstract: A novel support vector machine (SVM) model combining kernel principal component analysis (KPCA) with genetic algorithm (GA) is proposed for intrusion detection. In the proposed model, a multi-layer SVM classifier is adopted to estimate whether the action is an attack, KPCA is used as a preprocessor of SVM to reduce the dimension of feature vectors and shorten training time. In order to reduce the noise caused by feature differences and improve the performance of SVM, an improved kernel function (N-RBF) is proposed by embedding the mean value and the mean square difference values of feature attributes in RBF kernel function. GA is employed to optimize the punishment factor C, kernel parameters @s and the tube size @? of SVM. By comparison with other detection algorithms, the experimental results show that the proposed model performs higher predictive accuracy, faster convergence speed and better generalization.
Journal ArticleDOI
Cyber risk and cybersecurity: a systematic review of data availability
Frank Cremer,Barry Sheehan,Michel Fortmann,Arash Negahdari Kia,Marty Manor Mullins,Finbarr Murphy,S. Materne +6 more
TL;DR: In this paper , the authors identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks, and they posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue.
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A literature review on one-class classification and its potential applications in big data
TL;DR: One-class classification (OCC) as mentioned in this paper is an approach to detect abnormal data points compared to the instances of the known class and can serve to address issues related to severely imbalanced datasets, which are especially very common in big data.
References
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Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Journal ArticleDOI
An introduction to ROC analysis
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Williamson, estimating the support of a high-dimensional distribution
TL;DR: The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm.
Book ChapterDOI
Data Clustering: 50 Years Beyond K-means
TL;DR: Cluster analysis as mentioned in this paper is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics, which is one of the most fundamental modes of understanding and learning.
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