Proceedings ArticleDOI
An Intrusion-Detection Model
Dorothy E. Denning
- pp 118-118
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TLDR
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.Abstract:
A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage. The model includes profiles for representing the behavior of subjects with respect to objects in terms of metrics and statistical models, and rules for acquiring knowledge about this behavior from audit records and for detecting anomalous behavior. The model is independent of any particular system, application environment, system vulnerability, or type of intrusion, thereby providing a framework for a general-purpose intrusion-detection expert system.read more
Citations
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Computer Intrusion Detection with Clustering and Anomaly Detection, Using ICA and Rough Fuzzy
TL;DR: An intrusion detection method that proposes independent component analysis (ICA) based feature selection heuristics and using rough fuzzy for clustering data and the experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset are discussed.
Proceedings ArticleDOI
Research of the campus E-government network security management
Li Jin,Li Ya Chen +1 more
TL;DR: This article mainly analyzes the problems and solutions of the campus E-government network security, studies the security issue and its root in school campus network and mainly probes into the key technique of the schoolcampus network security management system.
Proceedings ArticleDOI
Intrusion detection: a novel approach that combines boosting genetic fuzzy classifier and data mining techniques
TL;DR: An intelligent intrusion detection system (IDS) is proposed which is an integrated approach that employs fuzziness and two of the well-known data mining techniques: namely classification and association rule mining by using an iterative rule learning that extracts out rules from the data set.
Dissertation
Detecting worm mutations using machine learning
TL;DR: The findings of this work demonstrate that Support Vector Machines can be used to detect worm mutations, and that the optimal configuration for detection of worm mutations is to use a linear kernel with unnormalised bi-gram frequency counts.