Host-based intrusion detection using dynamic and static behavioral models
Dit-Yan Yeung,Yuxin Ding +1 more
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TLDR
This paper adopts an anomaly detection approach by detecting possible intrusions based on program or user profiles built from normal usage data using a scheme that can be justified from the perspective of hypothesis testing.About:
This article is published in Pattern Recognition.The article was published on 2003-01-01 and is currently open access. It has received 370 citations till now. The article focuses on the topics: Intrusion detection system & Anomaly detection.read more
Citations
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Anomaly-based network intrusion detection: Techniques, systems and challenges
TL;DR: The main challenges to be dealt with for the wide scale deployment of anomaly-based intrusion detectors, with special emphasis on assessment issues are outlined.
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Novelty detection: a review—part 1: statistical approaches
M. Markou,Sameer Singh +1 more
TL;DR: There are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.
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An overview of anomaly detection techniques: Existing solutions and latest technological trends
Animesh Patcha,Jung-Min Park +1 more
TL;DR: This paper provides a comprehensive survey of anomaly detection systems and hybrid intrusion detection systems of the recent past and present and discusses recent technological trends in anomaly detection and identifies open problems and challenges in this area.
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Review: A review of novelty detection
TL;DR: This review aims to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade.
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A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.
Markus Goldstein,Seiichi Uchida +1 more
TL;DR: This paper aims to be a new well-funded basis for unsupervised anomaly detection research by publishing the source code and the datasets, and reveals the strengths and weaknesses of the different approaches for the first time.
References
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A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
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A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
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An Intrusion-Detection Model
TL;DR: A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described, based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage.