Association Rule Mining for Intrusion Detection System: A Survey

Authors

  • D. Selvamani Research Scholar, Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India
  • V. Selvi Assistant Professor, Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajeat-2019.8.1.1065

Keywords:

Data Mining, Network based Intrusion Detection System, Association Rule Mining, Apriori Algorithm

Abstract

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.

References

L. Mehrotra, P. S. Saxena, and N. V. Doohan, "A Data Classification Model: For Effective Classification of Intrusion in an Intrusion Detection System Based on Decision Tree Learning Algorithm," in Information and Communication Technology for Sustainable Development, Springer, Singapore, 2018, pp. 61-66.

P. Santra et al., "Fuzzy Data Mining-Based Framework for Forensic Analysis and Evidence Generation in Cloud Environment," in Ambient Communications and Computer Systems, Springer, Singapore, 2018, pp. 119-129.

H. Chen and S. Kuo, "DoS Attack Pattern Mining Based on Association Rule Approach for Web Server," in International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Springer, Cham, 2018.

M. Ahmed et al., "Clustering and association rule mining-based traffic analysis and prediction of Dhaka," Int. J. Knowledge Engineering and Data Mining, vol. 5, no. 4, pp. 241-276, 2018.

K. E. Heraguemi, N. Kamel, and H. Drias, "Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies," Appl. Intell., vol. 45, no. 4, pp. 1021-1033, 2016.

L. Mehrotra and P. S. Saxena, "An Assessment Report on: Statistics-Based and Signature-Based Intrusion Detection Techniques," in Information and Communication Technology, Springer, Singapore, 2018, pp. 321-327.

N. Lu et al., "Intrusion Detection System Based on Evolving Rules for Wireless Sensor Networks," J. Sensors, 2018.

C. Gupta, A. Sinhal, and R. Kamble, "An Enhanced Associative Ant Colony Optimization Technique-based Intrusion Detection System," in Artificial Intelligence and Evolutionary Algorithms in Engineering Systems, Springer, New Delhi, 2015, pp. 541-553.

S. Mabu et al., "A random-forests-based classifier using class association rules and its application to an intrusion detection system," Artif. Life Robotics, vol. 21, no. 3, pp. 371-377, 2016.

N. Khamphakdee, N. Benjamas, and S. Saiyod, "Improving intrusion detection system based on snort rules for network probe attacks detection with association rules technique of data mining," J. ICT Res. Appl., vol. 8, no. 3, pp. 234-250, 2015.

S. Parkinson, V. Somaraki, and R. Ward, "Auditing file system permissions using association rule mining," Expert Syst. Appl., vol. 55, pp. 274-283, 2016.

H. P. Vinutha, B. Poornima, and B. M. Sagar, "Detection of Outliers Using Interquartile Range Technique from Intrusion Dataset," in Information and Decision Sciences, Springer, Singapore, 2018, pp. 511-518.

R. R. Tiwari, A. K. Singh, and V. Singh, "Self-Learning SIEM System Using Association Rule Mining," J. Adv. Database Manage. Syst., vol. 2, no. 2, pp. 10-23, 2015.

I. Dutt et al., "Real-Time Hybrid Intrusion Detection System Using Machine Learning Techniques," in Advances in Communication, Devices and Networking, Springer, Singapore, 2018, pp. 885-894.

S. Mabu, W. Li, and K. Hirasawa, "A Class Association Rule Based Classifier Using Probability Density Functions for Intrusion Detection Systems," J. Adv. Comput. Intell. Intell. Informatics, vol. 19, no. 4, pp. 555-566, 2015.

G. Kaur, A. Bansal, and A. Agarwal, "Wavelets Based Anomaly-Based Detection System or J48 and Naïve Bayes Based Signature-Based Detection System: A Comparison," in Ambient Communications and Computer Systems, Springer, Singapore, 2018, pp. 213-224.

V. Herrera-Semenets et al., "A data reduction strategy and its application on scan and backscatter detection using rule-based classifiers," Expert Syst. Appl., vol. 95, pp. 272-279, 2018.

X. Jie et al., "Anomaly behavior detection and reliability assessment of control systems based on association rules," Int. J. Critical Infrastruct. Prot., 2018.

G. Y. Chan, F. Chua, and C. S. Lee, "Intrusion detection and prevention of web service attacks for software as a service: Fuzzy association rules vs fuzzy associative patterns," J. Intell. Fuzzy Syst., vol. 31, no. 2, pp. 749-764, 2016.

A. Chandrashekhar and J. V. Kumar, "Fuzzy Min-Max Neural Network-Based Intrusion Detection System," in Proc. Int. Conf. Nano-electronics, Circuits & Communication Systems, Springer, Singapore, 2017.

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Published

20-01-2019

How to Cite

Selvamani, D., & Selvi, V. (2019). Association Rule Mining for Intrusion Detection System: A Survey. Asian Journal of Engineering and Applied Technology, 8(1), 20–24. https://doi.org/10.51983/ajeat-2019.8.1.1065