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Appears in:
Pages: 199-206
Publication year: 2009
ISBN: 978-84-612-9801-3
ISSN: 2340-1117

Conference name: 1st International Conference on Education and New Learning Technologies
Dates: 6-8 July, 2009
Location: Barcelona ,Spain

STEADY STATE GENETIC-BASED MACHINE LEARNING FOR NETWORK INTRUSION DETECTION (SSGBML-NID)

W. Al-Sharafat, R. Naoum

Al al-Bayt university (JORDAN)
Society has grown to rely on Internet services, and the number of Internet users increases every day. As more and more users become connected to the network, the window of opportunity for malicious users to do their damage becomes very great and lucrative. The objective of this research is to incorporate different techniques into classier system to detect and classify intrusion from normal network packet. Among several techniques, Steady State Genetic-based Machine Leaning Algorithm (SSGBML) will be used to detect intrusions. Where Steady State Genetic Algorithm (SSGA), Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and Zeroth Level Classifier system are investigated in this research. SSGA is used as a discovery mechanism instead of SGA. SGA replaces all old rules with new produced rule preventing old good rules from participating in the next rule generation. Zeroth Level Classifier System is used to play the role of detector by matching incoming environment message with classifiers to determine whether the current message is normal or intrusion and receiving feedback from environment. Finally, in order to attain the best results, Modified SSGA will enhance our discovery engine by using Fuzzy Logic to optimize crossover and mutation probability. The experiments and evaluations of the proposed method were performed with the KDD 99 intrusion detection dataset.
@InProceedings{ALSHARAFAT2009STE,
author = {Al-Sharafat, W. and Naoum, R.},
title = {STEADY STATE GENETIC-BASED MACHINE LEARNING FOR NETWORK INTRUSION DETECTION (SSGBML-NID)},
series = {1st International Conference on Education and New Learning Technologies},
booktitle = {EDULEARN09 Proceedings},
isbn = {978-84-612-9801-3},
issn = {2340-1117},
publisher = {IATED},
location = {Barcelona ,Spain},
month = {6-8 July, 2009},
year = {2009},
pages = {199-206}}
TY - CONF
AU - W. Al-Sharafat AU - R. Naoum
TI - STEADY STATE GENETIC-BASED MACHINE LEARNING FOR NETWORK INTRUSION DETECTION (SSGBML-NID)
SN - 978-84-612-9801-3/2340-1117
PY - 2009
Y1 - 6-8 July, 2009
CI - Barcelona ,Spain
JO - 1st International Conference on Education and New Learning Technologies
JA - EDULEARN09 Proceedings
SP - 199
EP - 206
ER -
W. Al-Sharafat, R. Naoum (2009) STEADY STATE GENETIC-BASED MACHINE LEARNING FOR NETWORK INTRUSION DETECTION (SSGBML-NID), EDULEARN09 Proceedings, pp. 199-206.
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