%0 Journal Article %1 chenusing %A Chen, Rung-Ching %A Cheng, Kai-Fan %A Hsieh, Chia-Fen %D 2009 %J International Journal of Network Security & Its Applications (IJNSA) %K AttackDetectionRate IntrusionDetectionSystem RoughSet SupportVectorMachine %N 01 %P 01-13 %T Using Rough Set and Support Vector Machine for Network Intrusion Detection %U http://airccse.org/journal/current.html %V 01 %X The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine) to detect intrusions. First, RST is used to preprocess the data and reduce the dimensions. Next, the featureswere selected by RST will be sent to SVM model to learn and test respectively. The method is effective todecrease the space density of data. The experiments will compare the results with different methods and show RST and SVM schema could improve the false positive rate and accuracy.

@article{chenusing, abstract = {The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine) to detect intrusions. First, RST is used to preprocess the data and reduce the dimensions. Next, the featureswere selected by RST will be sent to SVM model to learn and test respectively. The method is effective todecrease the space density of data. The experiments will compare the results with different methods and show RST and SVM schema could improve the false positive rate and accuracy.}, added-at = {2017-09-19T11:30:03.000+0200}, author = {Chen, Rung-Ching and Cheng, Kai-Fan and Hsieh, Chia-Fen}, biburl = {https://www.bibsonomy.org/bibtex/284a5985f6eea3ec2eb4a482fc8f1e936/jerlin_jensi}, interhash = {5d8b2c7831c390791714b1062d8de185}, intrahash = {84a5985f6eea3ec2eb4a482fc8f1e936}, issn = {09749330}, journal = {International Journal of Network Security & Its Applications (IJNSA)}, keywords = {AttackDetectionRate IntrusionDetectionSystem RoughSet SupportVectorMachine}, month = apr, number = 01, pages = {01-13}, timestamp = {2017-09-19T11:30:38.000+0200}, title = {Using Rough Set and Support Vector Machine for Network Intrusion Detection}, url = {http://airccse.org/journal/current.html}, volume = 01, year = 2009 }