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Content Provider | IEEE Xplore Digital Library |
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Author | Subaira, A.S. Anitha, P. |
Copyright Year | 2014 |
Description | Author affiliation: Dept. of Comput. Sci. & Eng., Dr. NGP Inst. of Technol., Coimbatore, India (Subaira, A.S.; Anitha, P.) |
Abstract | In spite of growing information system widely, security has remained one hard-hitting area for computers as well as networks. In information protection, Intrusion Detection System (IDS) is used to safeguard the data confidentiality, integrity and system availability from various types of attacks. Data mining is an efficient artifice applied to intrusion detection to ascertain a new outline from the massive network data as well as it used to reduce the strain of the manual compilations of the normal and abnormal behavior patterns. This piece of writing reviews the present state of data mining techniques and compares various data mining techniques used to implement an intrusion detection system such as, Support Vector Machine, Genetic Algorithm, Neural network, Fuzzy Logic, Bayesian Classifier, K-Nearest Neighbor and decision tree Algorithms by highlighting a advantage and disadvantages of each of the techniques. |
Starting Page | 274 |
Ending Page | 280 |
File Size | 162357 |
Page Count | 7 |
File Format | |
e-ISBN | 9781479938377 |
DOI | 10.1109/ISCO.2014.7103959 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-01-10 |
Publisher Place | India |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Training Support vector machines Intrusion detection Classification Data Mining Classification algorithms Data mining Decision trees Intrusion Detection System Biological neural networks |
Content Type | Text |
Resource Type | Article |
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