A Study on Intrusion Detection System Techniques in CLOUD

Aramudhan M *, N. Thirmoorthi**, V. Suresh Kumar***, S.S. Mourougan****, S. JayaPrakash*****
* Associate Professor, PKIET
**_** Research Scholar, Manonmaniam Sundaranar University, Trinelveli, Tamil Nadu, India
**** Research Scholar, Periyar University, Salem, Tamil Nadu, India.
***** Research Scholar, Bharathiyar University, Coimbatore, Tamil Nadu, India.
Periodicity:January - March'2013
DOI : https://doi.org/10.26634/jse.7.3.2168

Abstract

Cloud environment requires better security due to the exponential growth of information, policies, services, resources, application and users. Flooding attacks such as SYN flood, UDP flood, HTTP flood, and FIN-WAIT have been posing a dangerous threat to Web servers, DNS servers, Mail servers, VoIP servers, etc. These flooding attacks reduce the limited capacity of the server resources and legal users could not able to access the resources of the server. Existing detection techniques used in Firewalls, IPS, IDS, etc., fail to identify the illegitimate traffic due to its self-similarity nature of legitimate traffic, suffer from low detection accuracy and high false alarms. Hence, automatic self intelligent mechanism is needed to identify these attacks that reduce the performance of the server. Intrusion Detection System (IDS) is an updatable, extensible and flexible security component that essential needs for protecting resources from illegitimate traffic and users in cloud environment. This paper deals with the existing computational techniques available with respect to IDS in cloud, their merits and demerits. The contents of this study should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of IDSs.

Keywords

Intrusion Detection System, Security Threats, Cloud, Accounting Models.

How to Cite this Article?

Aramudhan, M., Thirmoorthi, N., Kumar, S. V., Mourougan, S. S., and Jayaprakash, S. (2013). A Study on Intrusion Detection System Techniques in CLOUD. i-manager’s Journal on Software Engineering, 7(3), 1-8. https://doi.org/10.26634/jse.7.3.2168

References

[1]. Nguyen et al. (2012). “A collaborative Intrusion Detection System Framework for Cloud Computing”, Proceedings of the International Conference on IT convergence and Security 2011, Lecture notes in Electrical Engineering Volume 120, 2012, pp 91-109.
[2]. Ifran Gul et al. (2011). “Distributed Cloud Intrusion Detection Model”, International journal of Advanced Science and Technology, Vol.34 September 2011 pp 71- 82.
[3]. S.V.Narwane, (2012). “Intrusion Detection System in Cloud Computing Environment ”, International Conference on Advances in Communication and Computing Technologies ( ICACACT) 2012 PP 9 – 17.
[4]. Jun-Ho Lee et al. (2011). “Multi-level Intrusion Detection System and Log Management in Cloud Computing,“ International Journal on advanced communication Technology, 2011 PP 88-100.
[5]. Yizhang Guan et al. (2011). “A CP Intrusion Detection Strategy on Cloud Computing”, Proceedings of the International Symposium on Web Information Systems and Applications(WISA'09) May 22-24 PP.84-87.
[6]. Hatem Hamad et al, (2012). “Managing Intrusion Detection as a Service in Cloud Networks”, International Journal of Computer Applications, Volume 41-No.1, March 2012.PP 35-40.
[7]. S. Roschke, F. Cheng, and Ch. Meinel, (2009). “Intrusion Detection in the Cloud," in Eighth IEEE International Conference on Dependable, Autonomic, and Secure Computing, 2009, pp. 729-734.
[8]. Sudhir N. Dhage et al. (2012). “Intrusion Detection System in Cloud Computing Environment”, International Journal of Cloud Computing 2012 Vol.1 No.2/3 PP.261- 282.
[9]. Bakshi.A et al., (2010). “Securing Cloud from DDoS Attacks using Intrusion Detection System in Virtual Machine,” International conference on communication software and Networks 2010 pp.260-264.
[10]. Sanjay Ram M et al. (2012). “Effective Analysis of Cloud Based Intrusion Detection System”, International Journal of Computer Applications and Information Technology, Vol.1 sep. 2012 pp 16-22.
[11]. Chi-chun Lo et al. (2010). “A Cooperative Intrusion Detection System Framework for Cloud Computing Networks”, Proceedings of ICPPW'10 2010 PP 280-284.
[12]. Yassin, W et al. (2012). ”A cloud based Intrusion Detection Service Framework”, International Conference on cyber security, cyber warfare and Digital Forensic 2012, PP 213-218.
[13]. Sanjay Ram M, (2012). “Secure Cloud Computing based on Mutual Intrusion Detection System”, International Journal of Computer application Vol.1 Issue 2. Feb 2012 pp 57-67.
[14]. Hassen Mohammed Alsafi et al. (2011).” IDPS: An Integrated Intrusion Handling Model for Cloud Computing Environment” International journal of computing and amp. 2011 pp:90-108.
[15]. Brown DJ, Suckow B, Wang T, (2002). “A Survey of Intrusion Detection Systems”. Department of Computer Science, University of California, San Diego; 2002.
[16]. Ibrahim LM. (2010). ”Anomaly network intrusion detection system based on distributed time-delay neural network ”, Journal of Engineering Science and Technology 2010; 5(4):457–71.
[17]. Cannady, (1998). ”J. Artificial neural networks for misuse detection”, National Information Systems Security Conference, 1998.
[18]. Juan Jose Garcia Adeva, et al., (2007). “Intrusion Detection in Web applications using Text Mining”, Engineering applications of Artifical Intelligence 20 (2007) pp. 555-566.
[19]. Gang Wang et al., (2010). “A new approach to Intrusion Detection using Artificial Neural Networks and fuzzy clustering”, Journal of expert systems with applications 2010.
[20]. Gong RH, Zulkernine M, Abolmaesumi P. A (2005). software implementation of a genetic algorithm based approach to network intrusion detection. In: Proceedings of the sixth international conference on software engineering, artificial intelligence, networking and parallel/distributed computing and first ACIS international workshop on self-assembling wireless networks (SNPD/SAWN'05); 2005.
[21]. Dhanalakshmi Y, Ramesh Babu I. (2008). Intrusion detection using data mining along fuzzy logic and genetic algorithms. International Journal of Computer Science & Security, 2008;8(2):27–32.
[22]. Deepa Krishnan, (2012). “An Adaptive Distributed Intrusion Detection System for Cloud Computing Framework”, Proceedings of Recent Trends in Computer N e t w o r k s a n d D i s t r i b u t e d S y s t e m s S e c u r i t y communications in Computer and Information Science, Volume 335, 2012 pp 466-473.
[23]. S.Venkatesan, M.S.Saleem Basha, C. Chellappan, Anurika Vaish, P.Dhavachelvan, (2012). “Analysis of Accounting Models for the detection of duplicate requests in Web Services”, Journal of Kind Saud University- Computer and Information Sciences, May 2012 pp 1-18.
[24]. Alfantookh, Abdulkader. A., (2006). DoS attacks intelligent detection using neural networks. Journal of King Saud University -Computer and Information Science 18, 27–45.
[25]. Suriadi, Suriadi, Clark, Andrew, Schmidt, Desmond, (2010). ''Validating denial of service vulnerabilities in web services''. In: Proceedings of 2010 Fourth International Conference on Network and System, Security, pp. 175–182.
[26]. Xu, Donghua, Lu, Chenghuai, Dos Santos, Andre, (2002). ''Protecting web usage of credit cards using onetime pad cookie encryption''.In: Proceedings of the 18th Annual Computer Security Applications Conference (ACSAC'02), pp. 51–58
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