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Proceedings ArticleDOI

An Intrusion-Detection Model

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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.

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Reflecting on the Use of Sonification for Network Monitoring

TL;DR: This paper describes and reflects critically on the shortcomings of traditional network-monitoring methods and identifies the key role that sonification, if implemented correctly, could play in improving current monitoring capabilities.
Book ChapterDOI

A Metamodel for Web Application Injection Attacks and Countermeasures

TL;DR: The scope of the model is to provide low-effort guidance on an abstraction level of use for an enterprise decision maker when deciding between different countermeasures for web application injection attacks.

Insider Threat: User Identification Via Process Profiling

TL;DR: An insider threat detection system based on the Näıve Bayes method which examines process usage data and creates individual profiles for users is developed and is able to determine who is controlling the workstation with high accuracy.
Journal ArticleDOI

Research and Application of an improved Support Vector Clustering Algorithm on Anomaly Detection

Sheng Sun, +1 more
- 03 Jan 2010 - 
TL;DR: A novel weighted support vector clustering algorithm for anomaly detection that has excellent capability and applying it in intrusion detection system can be an effective way via using the data sets of KDD cup 99.
Proceedings ArticleDOI

Host-based intrusion detection by monitoring Windows registry accesses

TL;DR: The results of this study show that the proposed system is effective in detecting the behavior of malicious software and has a low rate of false alarms compared to other host-based intrusion detection systems.