Keywords

Data Mining, Intrusion Detection Systems, Anomaly Detection, Network Modeling

Abstract

Computer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal – firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other hand, anomaly-based ID systems determine what is normal traffic within a network and reports abnormal traffic behavior. This paper will describe a methodology towards developing a more-robust Intrusion Detection System through the use of data-mining techniques and anomaly detection. These data-mining techniques will dynamically model what a normal network should look like and reduce the false positive and false negative alarm rates in the process. We will use classification-tree techniques to accurately predict probable attack sessions. Overall, our goal is to model network traffic into network sessions and identify those network sessions that have a high-probability of being an attack and can be labeled as a "suspect session." Subsequently, we will use these techniques inclusive of signature detection methods, as they will be used in concert with known signatures and patterns in order to present a better model for detection and protection of networks and systems.

Notes

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Graduation Date

2005

Semester

Fall

Advisor

Wang, Morgan

Degree

Doctor of Philosophy (Ph.D.)

College

College of Arts and Sciences

Degree Program

Modeling and Simulation

Format

application/pdf

Identifier

CFE0000906

URL

http://purl.fcla.edu/fcla/etd/CFE0000906

Language

English

Release Date

May 2006

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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