Open Access Subscription Access
Open Access Subscription Access
Intrusion Detection Using Data Mining in Cloud Computing Environment
Nowadays cloud computing is widely accepted a paradigm. At present large amount of data is transferred between cloud and user and vice-versa. That data in transient is exposed to various intrusions. Therefore, security is the primary concern of cloud computing environment. Firewall and other security techniques can act as first line of defence and cannot provide a robust security solution. Intrusion detection systems proved to be best solutions to various attacks. Data mining techniques have emerged to make it less vulnerable and thus to analyze data and to determine various kind of attack. Both signatures based and anomaly based techniques effectively and efficiently used data mining techniques for any kind of attack detection. This paper presents various data mining techniques used in intrusion detection. This paper also reviews various cloud intrusion detection systems that uses data mining techniques for attack detection.
Cloud Computing, Cloud Intrusion Detection Systems, Data Mining, IDS, Intrusion Detection.
- I. Gul, and M. Hussain, “Distributed cloud intrusion detection model,” International Journal of Advanced Science and Technology, vol. 34, September 2011.
- U. Oktay, and O. K. Sahingoz, “Attack types and intrusion detection systems in cloud computing,” In 6th International Information Security & Cryptology Conference, Turkey, pp. 71-76, 20-21 September 2013.
- F. Gens, “New IDC IT cloud service survey: Top benefits and challenges, IDC exchange,” 2009. Available: http://blogs.idc.com/ie/?p=730S
- L. Martin, White Paper, 2010. Available: /http://www.lockheedmartin.com/data/assets/isgs/documents/CloudComputingWhitePaper.pdf
- J. Han, and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann, 2006.
- H. J. Patel, and R. Patel, “A Survey on intrusion detection system in cloud,” International Journal of Engineering and Technical Research (IJETR), vol. 2, no. 5, pp. 38-39, May 2014.
- P. Berkhin, “A survey of clustering data mining techniques,” Grouping Multidimensional Data, Springer Berlin Heidelberg, pp. 25-71, 2006.
- S. Agrawal, and J. Agrawal, “Survey on anomaly detection using data mining techniques,” Procedia Computer Science, vol. 60, pp. 708-713, 2015.
- P. Ganeshkumar, and N. Pandeeswari, “Adaptive neuro-fuzzy-based anomaly detection system in cloud,” International Journal of Fuzzy Systems, vol. 18, no. 3, pp. 367-378, June 2016.
- C. N. Modi, D. R. Patel, A. Patel, and R. Muttukrishnan, “Bayesian classifier and snort based network intrusion detection system in cloud computing,” Third International Conference on Computing, Communication and Networking Technologies, 26-28 July 2012.
- C. N. Modi, D. R. Patel, A. Patel, and R. Muttukrishnan, “Integrating signature apriori based Network Intrusion Detection System (NIDS) in cloud computing,” Procedia Technology, vol. 6 pp. 905-912, 2012.
- N. Pandeeswari, and G. Kumar, “Anomaly detection system in cloud environment using fuzzy clustering based ANN,” Mobile Networks and Applications, vol. 21, no. 3, pp. 494-505, June 2016.
- K. Vieira, A. Schulter, C. B. Westphall, and C. M. Westphall, “Intrusion detection for grid and cloud computing,” IT Professional, IEEE, vol. 12, no. 4, pp. 38-43, July-August 2010.
- Z. Chiba, N. Abghour, K. Moussaid, A. E. Omri, and M. Rida, “A cooperative and hybrid network intrusion detection framework in cloud computing based on snort and optimized back propagation neural network,” Procedia Computer Science, vol. 83, pp. 1200-1206, 2016.
- S. Raja, and S. Ramaiah, “An efficient fuzzy-based hybrid system to cloud intrusion detection,” International Journal of Fuzzy Systems, vol. 19, no. 1, pp. 62-77, February 2017.
Abstract Views: 13
PDF Views: 0