AN IMPROVED NETWORK INTRUSION DETECTION

TECHNIQUE BASED ON K-MEANS CLUSTERING VIA

NAIVE BAYES CLASSIFICATION

YOUSEF EMAMIYousef.emami@ieee.org

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AGENDA

Intrusion Detection

Dataset Description

THE PROPOSED MODEL FOR NIDS

EXPERIMENT AND RESULTS

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INTRUSION DETECTION

An Intrusion Detection System (IDS) inspects the activities in a system for suspicious behaviour or patterns that may indicate system attack or misuse.

There are two main categories of intrusion detection techniques;

Anomaly detection Misuse detection

Here ,the performance of K-means clustering and naïve classifier when trained to identify signature of specific attacks is reviewed.

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DATASET DESCRIPTION

The utilized data set is KDD Cup which contained a wide variety of intrusions simulated in a military network environment

It consisted of approximately 4,900,000 data instances

The simulated attacks fell in one of the following four categories:

DOS-Denial of Service (e.g. a syn flood), R2L- Unauthorized access from a remote machine (e.g. password

guessing), U2R-Unauthorized access to super user or root functions (e.g. a buffer

overflow attack) Probing-surveillance and other probing for vulnerabilities (e.g. port

scanning).

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K-MEANS CLUSTERING VIA NAIVE BAYES CLASSIFICATION MODEL FOR NIDS

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Metrics

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Thank you for your kind attention

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REFERENCE

• Sanjay Kumar Sharmai, Pankaj Pande, Susheel Kumar Tiwari and Mahendra Singh Sisodiai,”An Improved Network Intrusion Detection Technique based on k-Means Clustering via NaIve Bayes Classification”, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012) March 30, 31, 2012

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