Asian Journal of Engineering and Applied Technology (AJEAT)
Association Rule Mining for Intrusion Detection System: A SurveyAuthor : D. Selvamani and V. Selvi
Volume 8 No.1 January-March 2019 pp 20-24
Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundreds of attributes, network audit databases face tens of attributes. So the typical Apriori algorithm of association rule mining, which needs so many database scans, can be improved, dealing with such characteristics of transaction database. In this paper, a literature survey on the Association Rule Mining has carried out.
Data Mining, Network based Intrusion Detection System, Association Rule Mining, Apriori Algorithm
 Mehrotra, Latika, Prashant Sahai Saxena, and Nitika Vats Doohan, “A Data Classification Model: For Effective Classification of Intrusion in an Intrusion Detection System Based on Decision Tree Learning Algorithm”, Information and Communication Technology for Sustainable Development. Springer, Singapore, pp. 61-66, 2018.
 Santra, Palash, et al., “Fuzzy Data Mining-Based Framework for Forensic Analysis and Evidence Generation in Cloud Environment”, Ambient Communications and Computer Systems. Springer, Singapore, pp. 119-129, 2018.
 Chen, Hsing-Chung, and Shyi-Shiun Kuo, “DoS Attack Pattern Mining Based on Association Rule Approach for Web Server”, International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. Springer, Cham, 2018.
 Ahmed, Muyeed, et al., “Clustering and association rule mining-based traffic analysis and prediction of Dhaka”, International Journal of Knowledge Engineering and Data Mining, Vol. 5 No. 4, pp. 241-276, 2018.
 Heraguemi, Kamel Eddine, Nadjet Kamel, and Habiba Drias, “Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies”, Applied Intelligence, Vol. 45, No. 4, pp.1021-1033, 2016.
 Mehrotra, Latika, and Prashant Sahai Saxena, “An Assessment Report on: Statistics-Based and Signature-Based Intrusion Detection Techniques”, Information and Communication Technology. Springer, Singapore, pp. 321-327, 2018.
 Lu, Nannan, et al., “Intrusion Detection System Based on Evolving Rules for Wireless Sensor Networks”, Journal of Sensors, 2018.
 Gupta, Chetan, Amit Sinhal, and Rachana Kamble, “An Enhanced Associative Ant Colony Optimization Technique-based Intrusion Detection System”, Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Springer, New Delhi, pp. 541-553, 2015.
 Mabu, Shingo, et al., “A random-forests-based classifier using class association rules and its application to an intrusion detection system”, Artificial Life and Robotics, Vol. 21, No. 3, pp. 371-377, 2016.
 Khamphakdee, Nattawat, Nunnapus Benjamas, and Saiyan Saiyod, “Improving intrusion detection system based on snort rules for network probe attacks detection with association rules technique of data mining”, Journal of ICT Research and Applications, Vol. 8, No. 3, pp. 234-250, 2015.
 Parkinson, Simon, Vassiliki Somaraki, and Rupert Ward, “Auditing file system permissions using association rule mining”, Expert Systems with Applications, Vol 55, pp. 274-283, 2016.
 Vinutha, H. P., B. Poornima, and B. M. Sagar, “Detection of Outliers Using Interquartile Range Technique from Intrusion Dataset”, Information and Decision Sciences. Springer, Singapore, pp. 511-518, 2018.
 Tiwari, Ravi Raman, Anil Kumar Singh, and Vrijendra Singh, “Self-Learning SIEM System Using Association Rule Mining”, Journal of Advanced Database Management & Systems, Vol. 2, No. 2, pp. 10-23, 2015.
 Dutt, Inadyuti, et al., “Real-Time Hybrid Intrusion Detection System Using Machine Learning Techniques”, Advances in Communication, Devices and Networking. Springer, Singapore, pp. 885-894, 2018.
 Mabu, Shingo, Wenjing Li, and Kotaro Hirasawa, “A Class Association Rule Based Classifier Using Probability Density Functions for Intrusion Detection Systems”, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 19, No. 4, pp. 555-566, 2015.
 Kaur, Gagandeep, Amit Bansal, and Arushi Agarwal, “Wavelets Based Anomaly-Based Detection System or J48 and Naïve Bayes Based Signature-Based Detection System: A Comparison”, Ambient Communications and Computer Systems. Springer, Singapore, pp. 213-224, 2018.
 Herrera-Semenets, Vitali, et al., “A data reduction strategy and its application on scan and backscatter detection using rule-based classifiers”, Expert Systems with Applications, Vol. 95, pp. 272-279, 2018.
 Jie, Xinchun, et al., “Anomaly behavior detection and reliability assessment of control systems based on association rules”, International Journal of Critical Infrastructure Protection, 2018.
 Chan, Gaik-Yee, Fang-Fang Chua, and Chien-Sing Lee, “Intrusion detection and prevention of web service attacks for software as a service: Fuzzy association rules vs fuzzy associative patterns”, Journal of Intelligent & Fuzzy Systems, Vol. 31, No. 2, pp. 749-764, 2016.
 Chandrashekhar, Azad, and Jha Vijay Kumar, “Fuzzy Min-Max Neural Network-Based Intrusion Detection System”, Proceedings of the International Conference on Nano-electronics, Circuits & Communication Systems. Springer, Singapore, 2017.