CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Intrusion Detection Techniques in Cloud Computing: A Review

by Nurudeen Mahmud Ibrahim, Anazida Zainal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 12
Year of Publication: 2018
Authors: Nurudeen Mahmud Ibrahim, Anazida Zainal
10.5120/ijca2018916139

Nurudeen Mahmud Ibrahim, Anazida Zainal . Intrusion Detection Techniques in Cloud Computing: A Review. International Journal of Computer Applications. 179, 12 ( Jan 2018), 26-33. DOI=10.5120/ijca2018916139

@article{ 10.5120/ijca2018916139,
author = { Nurudeen Mahmud Ibrahim, Anazida Zainal },
title = { Intrusion Detection Techniques in Cloud Computing: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 179 },
number = { 12 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 26-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number12/28853-2018916139/ },
doi = { 10.5120/ijca2018916139 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:10.945073+05:30
%A Nurudeen Mahmud Ibrahim
%A Anazida Zainal
%T Intrusion Detection Techniques in Cloud Computing: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 12
%P 26-33
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a review of cloud-based intrusion detection system was provided. The review gives a detailed taxonomy of the existing approaches adopted by researchers in cloud intrusion detection system. The components of the taxonomy are the detection domain, detection technique, strategy for creating normal profile the architectural structure adopted by the intrusion detection system and the detection time. Based on the review open problems and research direction in cloud intrusion detection was provided.

References
  1. Mell, P., and Tim G. 2011. The NIST definition of cloud computing.
  2. Gupta, S., and Padam K.. 2015. An Immediate System Call Sequence Based Approach for Detecting Malicious Program Executions in Cloud Environment. Wireless Personal Communications 81(1):405-425.
  3. Tsai, W. K, Xin, S., and Janaka B. 2010. Service-oriented cloud computing architecture. Information Technology: New Generations (ITNG), 2010 Seventh International Conference on, 2010, pp. 684-689. IEEE.
  4. Chonka, A., Xiang,Y. Zhou, W. and Bonti A. 2011. Cloud security defense to protect cloud computing against HTTP-DoS and XML-DoS attacks. Journal of Network and Computer Applications 34(4):1097-1107.
  5. Khorshed, M., T, Shawkat A, and Saleh A W. 2012. A survey on gaps, threat remediation challenges and some thoughts for proactive attack detection in cloud computing. Future Generation computer systems 28(6):833-851.
  6. Sen, J. 2013. Security and privacy issues in cloud computing. Architectures and Protocols for Secure Information Technology Infrastructures: 1-45.
  7. Osanaiye, O., Kim-Kwang R. C., and Mqhele, D. 2016.Distributed denial of service (DDoS) resilience in cloud: review and conceptual cloud DDoS mitigation framework. Journal of Network and Computer Applications 67:147-165.
  8. Patel, A., Taghavi, M. Bakhtiyari, K. JúNior, J.C. 2013. An intrusion detection and prevention system in cloud computing: A systematic review. Journal of network and computer applications 36(1):25-41.
  9. Modi, C., Patel, D., Borisaniya, B. Patel, H., Patel, A., Rajarajan, M., 2013. A survey of intrusion detection techniques in cloud. Journal of Network and Computer Applications 36(1):42-57.
  10. Raghav, I., Shashi C., and Nitasha H. 2013. Intrusion Detection and Prevention in Cloud Environment: A Systematic Review. International Journal of Computer Applications 68(24).
  11. Sari, A. 2015. A Review of Anomaly Detection Systems in Cloud Networks and Survey of Cloud Security Measures in Cloud Storage Applications. Journal of Information Security 6(02):142.
  12. Scarfone, K., and Peter M. 2007. Guide to intrusion detection and prevention systems (idps). NIST special publication 800(2007):94.
  13. Yeung, D., and Yuxin, D. 2003. Host-based intrusion detection using dynamic and static behavioral models. Pattern recognition 36(1):229-243.
  14. Kruegel, C., and Thomas T. 2000. A survey on intrusion detection systems. TU Vienna, Austria, 2000. Citeseer.
  15. Ficco, M., Luca Tasquier, and Rocco Aversa 2013. Intrusion detection in cloud computing. P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2013 Eighth International Conference on, 2013, pp. 276-283. IEEE.
  16. Tupakula, Udaya, Vijay V., and Naveen A. 2011. Intrusion detection techniques for infrastructure as a service cloud. Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE Ninth International Conference on, 2011, pp. 744-751. IEEE.
  17. Chouhan, P. K., Haggan M.,McWilliams G. 2011 Network Based Malware Detection within Virtualised Environments. Euro-Par 2014: Parallel Processing Workshops, 2014, pp. 335-346. Springer.
  18. Garcia-Teodoro, P., Diaz, J. Verdejo, G.M. Fernandez, E. V. 2009. Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & security 28(1):18-28.
  19. Denning, D. E, and Peter, G. N. 1985. Requirements and model for IDES—a real-time intrusion detection expert system. Document A005, SRI International 333.
  20. Zakarya, M. 2013. DDoS Verification and Attack Packet Dropping Algorithm in Cloud Computing. World Applied Sciences Journal 23(11):1418-1424.
  21. Ismail, M. N., Aborujilah A., Musa, S. 2013. Detecting flooding based DoS attack in cloud computing environment using covariance matrix approach. Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, 2013, pp. 36. ACM.
  22. Gupta, S., Padam K., and Ajith A. 2011. A profile based network intrusion detection and prevention system for securing cloud environment. International Journal of Distributed Sensor Networks 2013.
  23. Shamsolmoali, P., and Masoumeh Z.. 2014. Statistical-based filtering system against DDOS attacks in cloud computing. Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on, 2014, pp. 1234-1239. IEEE.
  24. Estevez-Tapiador, J. M, Pedro G. T. and Jesus E. D. 2003. Stochastic protocol modeling for anomaly based network intrusion detection. Information Assurance, 2003. IWIAS 2003. Proceedings. First IEEE International Workshop on, 2003, pp. 3-12. IEEE.
  25. Heckerman, D. 1998. A tutorial on learning with Bayesian networks: Springer.
  26. Kruegel, C. M., Darren, R. , Fredick, V. 2003. Bayesian event classification for intrusion detection. Computer Security Applications Conference, 2003. Proceedings. 19th Annual, 2003, pp. 14-23. IEEE.
  27. Hu, J., Yu,X. , Qiu, H. Chen. 2009. A simple and efficient hidden Markov model scheme for host-based anomaly intrusion detection. Network, IEEE 23(1):42-47.
  28. Alarifi, S., and Stephen W. 2013. Anomaly detection for ephemeral cloud IaaS virtual machines. In Network and system security. Pp. 321-335: Springer.
  29. Pandeeswari, N, and Ganesh K. 2015. Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN. Mobile Networks and Applications: 1-12.
  30. Xiong W,Y. Laurence, P. Wen-Chih, W, Xiofei, Q. Yanzhen 2014. Anomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communications. Information Sciences 258:403-415.
  31. Mandi G, Sirhind 2013. Survey paper on data mining techniques of intrusion detection.
  32. Shirazi, N., Simpson S., Marnerides, A., Watson, M., Mauthe A.,and Hutchison D. 2014. Assessing the impact of intra-cloud live migration on anomaly detection. Cloud Networking (CloudNet), 2014 IEEE 3rd International Conference on, 2014, pp. 52-57. IEEE.
  33. Vapnik, Vladimir Naumovich, and Vlamimir Vapnik1998. Statistical learning theory. Volume 1: Wiley New York.
  34. Furey, T. S., Cristianini N., Duffy, N., Bednarski, D.W.. 2000. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10):906-914.
  35. Adamova, Kirila, S. Dominik, P. Benhard, S.Paul 2014. Network anomaly detection in the cloud: The challenges of virtual service migration. Communications (ICC), 2014 IEEE International Conference on, 2014, pp. 3770-3775. IEEE
  36. Lo, C.-C., Huang, C.-C. and Ku, J. (2010). A cooperative intrusion detection system framework for cloud computing networks. Proceedings of the 2010b 2010 39th International Conference on Parallel Processing Workshops, 280-284.
  37. Man, N. D. and Huh, E.-N. (2012). A collaborative intrusion detection system framework for cloud computing. Proceedings of the 2012 Proceedings of the International Conference on IT Convergence and Security 2011, 91-109.
  38. Huang, T., Zhu, Y., Wu, Y., Bressan, S. and Dobbie, G. (2016). Anomaly detection and identification scheme for VM live migration in cloud infrastructure. Future Generation Computer Systems, 56, 736-745.
  39. Muthurajkumar, S., Kulothungan, K., Vijayalakshmi, M., Jaisankar, N. and Kannan, A. (2013). A Rough Set based Feature Selection Algorithm for Effective Intrusion Detection in Cloud Model.
  40. Maiti, S., Garai, C. and Dasgupta, R. (2015). A detection mechanism of DoS attack using adaptive NSA algorithm in cloud environment. Proceedings of the 2015 Computing, Communication and Security (ICCCS), 2015 International Conference on, 1-7.
  41. Zhou, L.-H., Liu, Y.-H. and Chen, G.-L. (2011). A feature selection algorithm to intrusion detection based on cloud model and multi-objective particle swarm optimization. Proceedings of the 2011 Computational Intelligence and Design (ISCID), 2011 Fourth International Symposium on, 182-185.
  42. Kannan, A., Maguire, G. Q., Sharma, A. and Schoo, P. (2012). Genetic algorithm based feature selection algorithm for effective intrusion detection in cloud networks. Proceedings of the 2012 Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on, 416-423.
  43. Bharadwaja, S., Sun, W., Niamat, M. and Shen, F. (2011). Collabra: a xen hypervisor based collaborative intrusion detection system. Proceedings of the 2011 Information technology: New generations (ITNG), 2011 eighth international conference on, 695-700.
  44. Giannakou, A., Rillling, L., Pazat, J.-L., Majorczyk, F. and Morin, C. (2015). Towards Self Adaptable Security Monitoring in IaaS Clouds. Proceedings of the 2015 Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on, 737-740.
  45. Toumi, H., Talea, A., Marzak, B., Eddaoui, A. and Talea, M. (2015). Cooperative trust framework for cloud computing based on mobile agents. International Journal of Communication Networks and Information Security, 7(2), 106
  46. Li, Z., Sun, W. and Wang, L. (2012). A neural network based distributed intrusion detection system on cloud platform. Proceedings of the 2012 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, 75-79.
  47. Nagarajan, P. and Perumal, G. (2015). A Neuro Fuzzy Based Intrusion Detection System for a Cloud Data Center Using Adaptive Learning. Cybernetics and Information Technologies, 15(3), 88-103.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud computing Security Review Taxonomy Intrusion Detection Techniques

Powered by PhDFocusTM