Category-Based Intrusion Detection Using PCA
作者： Gholam Reza Zargar , Tania Baghaie
关键词: Pattern recognition 、 Data mining 、 Artificial intelligence 、 Feature selection 、 Principal component analysis 、 Intrusion detection system 、 Feature (computer vision) 、 Computer science 、 IP header 、 Denial-of-service attack 、 Anomaly-based intrusion detection system 、 Identification (information)
摘要: Existing Intrusion Detection Systems (IDS) examine all the network features to detect intrusion or misuse patterns. In feature-based detection, some selected may found be redundant, useless less important than rest. This paper proposes a category-based selection of effective parameters for detection using Principal Components Analysis (PCA). this paper, 32 basic from TCP/IP header, and 116 derived TCP dump are in traffic dataset. Attacks categorized four groups, Denial Service (DoS), Remote User attack (R2L), (U2R) Probing attack. DARPA 1998 dataset is used experiments as PCA method determine an optimal feature set make process faster. Experimental results show that reduction can improve rate approach while maintaining accuracy within acceptable range. KNN classification attacks. will significantly speed up train testing periods identification attempts.
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