Fuzziness based semi-supervised learning approach for intrusion detection system

Information Sciences

Volumn 378, Issue , 2017, Pages 484-497

  Ashfaq, Rana Aamir Raza ^a   Wang, Xi Zhao ^a   Huang, Joshua Zhexue ^a   Abbas, Haider ^b   He, Yu Lin ^a  

^a SHENZHEN UNIVERSITY   (China)

^b KING SAUD UNIVERSITY   (Saudi Arabia)

Author keywords

Divide and conquer strategy; Fuzziness; Intrusion detection; Random weight neural network; Semi supervised learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER CRIME; DECISION TREES; FUZZY SET THEORY; FUZZY SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MERCURY (METAL); PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS; SUPERVISED LEARNING;

DIVIDE AND CONQUER; FUZZINESS; RANDOM WEIGHT; SEMI- SUPERVISED LEARNING; SEMI-SUPERVISED LEARNING (SSL); SINGLEHIDDEN LAYER FEED-FORWARD NEURAL NETWORK (SLFN); SUPERVISED AND UNSUPERVISED LEARNING; SUPERVISED LEARNING APPROACHES;

INTRUSION DETECTION;

EID: 84971261474     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2016.04.019     Document Type: Article

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