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Sengupta, Jyotsna
- Customized Way of Resource Discovery in a Campus Grid
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Authors
Affiliations
1 Society for Promotion of IT in Chandigarh (SPIC), Chandigarh, IN
2 Department of Computer Science, Punjabi University, Patiala, IN
1 Society for Promotion of IT in Chandigarh (SPIC), Chandigarh, IN
2 Department of Computer Science, Punjabi University, Patiala, IN
Source
International Journal of Advanced Networking and Applications, Vol 1, No 1 (2009), Pagination: 51-56Abstract
Campus Grid computing involves heterogeneous resources of an organization working in collaboration to solve the problems that cannot be addressed by a single resource. However, basic problem for Campus Grid users is how to discover the best resources required for the particular type of a job. There are various approaches using which Grid Discovery can be performed. This paper provides the grid resource discovery solutions for Campus Grid using Globus Toolkit which will enable us to customize the resource information according to the requirements based on the jobs to be run on the Campus Grid and present it in our own format. Here we propose building up our own service on top of globus MDS in order to process the information provided by MDS and use it in our Campus Grid Portal.Full Text
- WLI-FCM and Artificial Neural Network Based Cloud Intrusion Detection System
Abstract Views :185 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Punjabi University, Patiala, Punjab, IN
1 Department of Computer Science, Punjabi University, Patiala, Punjab, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No 1 (2018), Pagination: 3698-3703Abstract
Security and Performance aspects of cloud computing are the major issues which have to be tended to in Cloud Computing. Intrusion is one such basic and imperative security problem for Cloud Computing. Consequently, it is essential to create an Intrusion Detection System (IDS) to detect both inside and outside assaults with high detection precision in cloud environment. In this paper, cloud intrusion detection system at hypervisor layer is developed and assesses to detect the depraved activities in cloud computing environment. The cloud intrusion detection system uses a hybrid algorithm which is a fusion of WLI- FCM clustering algorithm and Back propagation artificial Neural Network to improve the detection accuracy of the cloud intrusion detection system. The proposed system is implemented and compared with K-means and classic FCM. The DARPA’s KDD cup dataset 1999 is used for simulation. From the detailed performance analysis, it is clear that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate.Keywords
Cloud Computing, Cloud Intrusion Detection System, Intrusion Detection System, IDS, Security.Full Text
References
- H. Jin, G, Xiang, D. Zou, S. Wu, F. Zhao, M. Li, And W. Zheng, AVMM-based intrusion prevention system in cloud computing environment, Journal of Supercomputing Springer, 66(3), 2011, 1133–1151.
- F. Gens, New IDC IT Cloud Service Survey: Top Benefits and Challenges Exchange, 2009, online; http://www.blogs.idc.com/ie/p=730S. (Accessed 12 may 2017).
- L. Martin, White Paper, 2010, online: http://www.Lockheedmartin.com/data/assets/isgs/documents/CloudComputingWhitePaper.pdf.
- C. Modi ,D. Patel, B. Borisaniya, H. Patel, A. Patel and M. Rajarajan, A survey of intrusion detection techniques in Cloud, Journal of Network and Computer Applications, 36(1), 2013, 42-57.
- K. Vieira, A. Schulter, C.B. Westphall, and C. M. Westphall, Intrusion detection techniques in grid and cloud computing environment. IEEE IT Professional Magazine , 2010, 38–43
- S.Raja and S. Ramaiah, An Efficient Fuzzy-Based Hybrid System to Cloud Intrusion Detection, International Journal of Fuzzy Systems, 19(1), 2016, 116.
- N. Pandeeswari and Ganesh Kumar, Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN, Mobile Networks and Applications, 21(3), 2016, 494-505.
- C. N. Modi, D. R. Patel, A. Patel, and M. Rajarajan, Integrating Signature Apriori based Network Intrusion Detection system (NIDS) in Cloud Computing. In: Proceedings of 2nd International Conference on Communication, Computing & Security, Procedia Technology, 6:905–912. Doi: 10.1016/j.protcy.2012.10.110
- C. C. Lo, C. C. Huang, and J. Ku , A Cooperative Intrusion Detection System Framework for Cloud Computing Networks, 39th International Conference on Parallel Processing Workshops , 2010, 280-284.
- Z. Chiba, N. Abghour, K. Moussaid and M. Rida, A Cooperative and Hybrid Network Intrusion Detection Framework in Cloud Computing Based on Snory and Optimized back Propagation neural Network, International Workshop on Mobile Cloud Computing Systems, Management and Security, 83, 2016, 1200-1206.
- C. Wu, C. Ouyang, L. Chen, and L. Lu, A New Fuzzy Clustering Validity Index with a Median Factor for Centroid-based Clustering, IEEE Transactions on Fuzzy Systems, 23(3),2015, 701 – 718.
- KDD Cup 1999. Available online: http://www.kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, October 2007.
- R. kulhare and D. Singh, Intrusion Detection System based on Fuzzy C Means Clustering and Probabilistic Neural Network, International Journal of Computer Applications, 74, 2013, 30-33.
- K. Nalavade and B. B. Mehsram, Evaluation of KMeans Clustering for Effective Intrusion Detection and Prevention in Massive Network Traffic Data, International Journal of Computer Applications, 96, 2014, 9-14.
- Intrusion Detection Using Data Mining in Cloud Computing Environment
Abstract Views :256 |
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Authors
Affiliations
1 Department of Computer Science, Punjabi University, Patiala, Punjab, IN
2 Kurukshetra University, Kurukshetra, Haryana, IN
1 Department of Computer Science, Punjabi University, Patiala, Punjab, IN
2 Kurukshetra University, Kurukshetra, Haryana, IN
Source
International Journal of Distributed and Cloud Computing, Vol 6, No 2 (2018), Pagination: 19-23Abstract
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.Keywords
Cloud Computing, Cloud Intrusion Detection Systems, Data Mining, IDS, Intrusion Detection.
References
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- 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.