Fuzzy Data Mining and Genetic Algorithms Applied to Intrusion Detection

By Bridges, Susan M., Dr.

Description
Technical Reference Publication

Excerpt
Introduction: The wide spread use of computer networks in today?s society, especially the sudden surge in importance of e-commerce to the world economy, has made computer network security an international priority. Since it is not technically feasible to build a system with no vulnerabilities, intrusion detection has become an important area of research. Intrusion detection approaches are commonly divided into two categories: misuse detection and anomaly detection [1]. The misuse detection approach attempts to recognize attacks that follow intrusion patterns that have been recognized and reported by experts. Misuse detection systems are vulnerable to intruders who use new patterns of behavior or who mask their illegal behavior to deceive the detection system. Anomaly detection methods were developed to counter this problem. With the anomaly detection approach, one represents patterns of normal behavior, with the assumption that an intrusion can be identified based on some deviation from this normal behavior.

Table of Contents
Conference Committee.........................................................................................................................i Table of Contents.................................................................................................................................ii Refereed Papers Next Generation Intrusion Detection: Autonomous Reinforcement ...........................File Size: 90 K Learning of Network Attacks James Cannady, Georgia Institute of Technology Fuzzy Data Mining and Genetic Algorithms Applied to Intrusion Detection..........File Size: 113 K Susan M. Bridges & Rayford B. Vaughn, Mississippi State University

Book Id: WPLBN0000659190
Format Type: PDF eBook
File Size: 113.19 KB.
Reproduction Date: 2005
Full Text

Title:

Fuzzy Data Mining and Genetic Algorithms Applied to Intrusion Detection

Author:

Bridges, Susan M., Dr.

Volume:

Language:

English

Subject:

Technology., Reference materials, Technology and literature

Collection:

Techonology eBook Collection

Subcollection:

Historic
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