ENHANCED INTRUSION DETECTION TECHNIQUE BASED ON MACHINE LEARNING
Yaseen, H., Abuelenin, S., Rashad, M. (2015). ENHANCED INTRUSION DETECTION TECHNIQUE BASED ON MACHINE LEARNING. EKB Journal Management System, 15(2), 31-43. doi: 10.21608/ijicis.2015.15755
H Yaseen; S Abuelenin; M Rashad. "ENHANCED INTRUSION DETECTION TECHNIQUE BASED ON MACHINE LEARNING". EKB Journal Management System, 15, 2, 2015, 31-43. doi: 10.21608/ijicis.2015.15755
Yaseen, H., Abuelenin, S., Rashad, M. (2015). 'ENHANCED INTRUSION DETECTION TECHNIQUE BASED ON MACHINE LEARNING', EKB Journal Management System, 15(2), pp. 31-43. doi: 10.21608/ijicis.2015.15755
Yaseen, H., Abuelenin, S., Rashad, M. ENHANCED INTRUSION DETECTION TECHNIQUE BASED ON MACHINE LEARNING. EKB Journal Management System, 2015; 15(2): 31-43. doi: 10.21608/ijicis.2015.15755

ENHANCED INTRUSION DETECTION TECHNIQUE BASED ON MACHINE LEARNING

International Journal of Intelligent Computing and Information Sciences
Article 3, Volume 15, Issue 2, April 2015, Page 31-43  XML
Document Type: Original Article
DOI: 10.21608/ijicis.2015.15755
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Authors
H Yaseen1; S Abuelenin2; M Rashad3
1Faculty of Computers & Information, Mansoura University - Egypt
2Faculty of Computer and Information,Mansoura University, Egypt
3Computer Science Department,Faculty of Computers and Information, Mansoura University, Egypt
Abstract
Intrusion leads to violations of the security policies of a computer system. An intrusion detection system (IDS) is a software application that monitors network or system activities for pernicious activities. Many researchers propose the intrusion detection based on machine learning techniques or neural networks, but some of them didn't introduce high detection or decrease the time. The proposed framework is based on machine learning algorithms. These algorithms, discernibility classifier based k-nearest, J48 decision tree and Naïve Bayes rule, are used to discover any intrusion based on anomaly detection. The primary aim of this paper is to enhance the strength of the overall classification decision in better results than any other existent techniques. The performance metrics in our experimental are accuracy, error rate, sensitivity, specificity, and Precision. We notice during experimental results by using NSL-KDD data set, there are improvements in almost results by using the proposed framework.
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