TY  - INPR
N1  - Bài báo này ?ă giành ???c gi?i th??ng "Best Paper Award" t?i h?i th?o SOIS 2017.
ID  - SisLab2759
UR  - http://sois2017.uit.edu.vn/
A1  - Nguyen, Xuan Nam
A1  - Nguyen, Dai Tho
Y1  - 2017/12/02/
N2  - In this paper, we proposed a method to extract more general features of data for payload-based anomaly IDS. However, because of the significant rise in the number of features, there are numerous redundancies, leading to the rise in the complexity and the decrease in the accuracy of the classification. To that end, we apply Chi square [9] feature selection method to pick up the best features in the feature set. We have done many experiments on real world dataset of HTTP-based attacks to evaluate the performance of our classifier using our feature extraction method. The results show that our classifier can quickly detect the attack packets with very high true positive rate while keeping the false positive rate at a very low level. Besides, the results also indicate that our classifier outperforms other classifiers such as McPAD [10], and PAY [12, 13].
TI  - Intrusion Detection Using a More General Feature Extraction Method for Payload-based Anomaly One-Class Classifier
M2  - Ho Chi Minh City, Vietnam
AV  - public
T2  - H?i th?o l?n th? II M?t s? v?n ?? ch?n l?c v? an toàn an ninh thông tin
ER  -