@article {10.3844/jcssp.2011.1560.1564,
article_type = {journal},
title = {A Novel Local Network Intrusion Detection System  Based on Support Vector Machine},
author = {Mohammad, Muamer N. and Sulaiman, Norrozila and Khalaf, Emad T.},
volume = {7},
number = {10},
year = {2011},
month = {Aug},
pages = {1560-1564},
doi = {10.3844/jcssp.2011.1560.1564},
url = {https://thescipub.com/abstract/jcssp.2011.1560.1564},
abstract = {Problem statement: Past few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable Intrusion Detection Systems (IDS). Many methods and techniques were used for modeling the IDS, but some of them contribute little or not to resolve it. Approach: Intrusion detection system for local area network by using Support Vector Machines (SVM) was proposed. First, the intrusion ways and intrusion connecting of Local Area Network were defined for putting forward the design requests on intrusion detection system of LAN. Second, the new method to recognized attack patterns which may give better coverage and make the detection more effective. Results and Conclusion: SVM was used as a detection system that recognizes anomalies and raises an alarm. The data that was used in our experiments originated from a campus lab. The result of the evaluation produced a better result in terms of the detection efficiency and false alarm rate.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}