TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Using weighted Support Vector Machine to address the imbalanced classes problem of Intrusion Detection System |
| 영문제목(English Title) |
Using weighted Support Vector Machine to address the imbalanced classes problem of Intrusion Detection System |
| 저자(Author) |
Alaeddin Alabdallah
Mohammed Awad
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| 원문수록처(Citation) |
VOL 12 NO. 10 PP. 5143 ~ 5158 (2018. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Improving the intrusion detection system (IDS) is a pressing need for cyber security world. With the growth of computer networks, there are constantly daily new attacks. Machine Learning (ML) is one of the most important fields which have great contribution to address the intrusion detection issues. One of these issues relates to the imbalance of the diverse classes of network traffic. Accuracy paradox is a result of training ML algorithm with imbalanced classes. Most of the previous efforts concern improving the overall accuracy of these models which is truly important. However, even they improved the total accuracy of the system; it fell in the accuracy paradox. The seriousness of the threat caused by the minor classes and the pitfalls of the previous efforts to address this issue is the motive for this work. In this paper, we consolidated stratified sampling, cost function and weighted Support Vector Machine (WSVM) method to address the accuracy paradox of ID problem. This model achieved good results of total accuracy and superior results in the small classes like the User-To-Remote and Remote-To-Local attacks using the improved version of the benchmark dataset KDDCup99 which is called NSL-KDD.
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| 키워드(Keyword) |
Intrusion detection system
Weighted Support Vector Machine
Stratified sampling
Cost function
NSL-KDD
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| 파일첨부 |
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