Title :
A novel signature searching for Intrusion Detection System using data mining
Author :
Ding, Ya-li ; Li, Lei ; Luo, Hong-qi
Author_Institution :
Pattern Recognition & Intell. Syst., Nanjing Univ. of Posts & Telecommun., Nanjing, China
Abstract :
Intrusion Detection System (IDS) has recently emerged as an important component for enhancing information system security. Data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. In this paper, we propose a novel signature searching to detect intrusion based on data mining, which is an improved Apriori algorithm. We evaluate the capability of this new approach with the data from KDD 1999 data mining competition. Our experimental results demonstrate the potential of the proposed method.
Keywords :
data mining; learning (artificial intelligence); security of data; Apriori algorithm; association rule; data mining; intrusion detection system; machine learning; network traffic data; signature searching; Association rules; Cybernetics; Data mining; Information systems; Intrusion detection; Itemsets; Machine learning; Machine learning algorithms; Pattern recognition; Protection; Apriori algorithm; Association rule; Data mining; Frequent itemset; Intrusion detection; Scenario;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212577