International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 76 - Issue 16 |
Published: August 2013 |
Authors: P. Amudha, S. Karthik, S. Sivakumari |
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P. Amudha, S. Karthik, S. Sivakumari . Classification Techniques for Intrusion Detection – An Overview. International Journal of Computer Applications. 76, 16 (August 2013), 33-40. DOI=10.5120/13334-0928
@article{ 10.5120/13334-0928, author = { P. Amudha,S. Karthik,S. Sivakumari }, title = { Classification Techniques for Intrusion Detection – An Overview }, journal = { International Journal of Computer Applications }, year = { 2013 }, volume = { 76 }, number = { 16 }, pages = { 33-40 }, doi = { 10.5120/13334-0928 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2013 %A P. Amudha %A S. Karthik %A S. Sivakumari %T Classification Techniques for Intrusion Detection – An Overview%T %J International Journal of Computer Applications %V 76 %N 16 %P 33-40 %R 10.5120/13334-0928 %I Foundation of Computer Science (FCS), NY, USA
Security is becoming a critical part of organizational information systems and security of a computer system or network is compromised when an intrusion takes place. In the field of computer networks security, the detection of threats or attacks is nowadays a critical problem to solve. Intrusion Detection Systems (IDS) have become a standard component in network security infrastructures and is an essential mechanism to protect computer systems from many attacks. In recent years, intrusion detection using data mining have attracted researchers more and more interests. Different researchers propose a different algorithm in different categories. Classifier construction is another research challenge to build an efficient intrusion detection system. KDDCup 1999 intrusion detection dataset plays a key role in fine tuning intrusion detection system and is most widely used by the researchers working in the field of intrusion detection. This paper presents an overview of intrusion detection, KDDCup'99 dataset and detailed analysis of classification techniques used in intrusion detection.