an improved network intrusion detection technique based on
TRANSCRIPT
AN IMPROVED NETWORK INTRUSION DETECTION
TECHNIQUE BASED ON K-MEANS CLUSTERING VIA
NAIVE BAYES CLASSIFICATION
YOUSEF [email protected]
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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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