Design a Hybrid Algorithm for Cloud Computing Security
[1]
Salah Talha Babiker, Department of Computer Engineering, College of Computers & IT, Taif University, Taif, Saudi Arabia.
[2]
Ayman ALI Abdalla ALI, Department of Computer Science, College of Computers & IT, TaIf University, Taif, Saudi Arabia.
Data are at the core of IT security concerns for any organization, whatever the form of infrastructure that is used. Cloud computing does not change this, but cloud computing does bring an added focus because of the distributed nature of the cloud computing infrastructure and the shared responsibilities that it involves. Security considerations apply both to data at rest (held on some form of storage system) and also to data in motion (being transferred over some form of communication link), both of which may need particular consideration when using cloud computing services. Essentially, the questions relating to data for cloud computing are about various forms of risk: risk of theft or unauthorized disclosure of data, risk of tampering or unauthorized modification of data, risk of loss or of unavailability of data. It is also worth remembering that in the case of cloud computing, "data assets" may well include things such as application programs or machine images, which can have the same risk considerations as the contents of databases [1, 2]. Cloud computing provides a framework for supporting end users easily attaching powerful services and applications through Internet. Denial of- Service (DoS) attack or Distributed Denial-of-Service (DDoS) are major security issues in cloud environment. [11, 18] Moreover, we present a distributed architecture for providing intrusion detection in Cloud Computing, which enables Cloud providers to offer security solutions as a service. It is a hierarchical and multi-layer architecture designed to collect information in the Cloud environment, using multiple distributed security components, which can be used to perform complex event correlation analysis.
Cloud, Encryption, Risk and Genetic Algorithm
[1]
M. Mezmaz, N. Melab, Y. Kessaci, Y.C. Lee, E.G. Talbi, A.Y. Zomaya, D. Tuyttens, A parallel biobjective hybrid metaheuristic for energyaware scheduling for cloud computing systems, Elsevier, Journal of Parallel and Distributed Computing, 71(11), 2011, pp.14971508.
[2]
M. Armbrust A. Fox, R. Griffith, A.D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, M. Zaharia, "A view of cloud computing." Communications of the ACM 53(4), 2010, pp. 5058.
[3]
Azure servicesplatform, Website, /http://www.microsoft.com/azureS; 2011. Amazon webservices, Website, /http://aws.amazon.comS; 2011.
[4]
Bahram S, Jiang X, Wang Z, Grace M. DKSM: subverting virtual machine introspection for fun and profit. In: Proceedings of the 29th IEEE international symposium on reliable distributed systems; 2010.
[5]
Li W. A genetic algorithm approach to network intrusion detection. USA: SANS Institute; 2004.
[6]
M.A. AlZain and E. Pardede, "Using Multi Shares for Ensuring Privacy in Database-as-a-Service", 44th Hawaii Intl. Conf. on System Sciences (HICSS), 2011, pp. 1-9.
[7]
M.D. Dikaiakos, D. Katsaros, P. Mehra, G. Pallis, A. Vakali, "Cloud computing: distributed internet computing for IT and scientific research." IEEE Internet Computing, 13(5), 2009, pp.1013.
[8]
S.T. Maguluri, R. Srikant, Y. Lei, Stochastic models of load balancing and scheduling in cloud computing clusters, IEEE Proceedings (INFOCOM), 2012, pp. 702710.
[9]
Q. Li and G. Yike, Optimization of Resource Scheduling in Cloud Computing, IEEE SYNASC, 2010, pp. 315320. 6. Z. Pooranian Z, A. Harounabadi, M. Shojafar, N. Hedayat, New hybrid algorithm for task scheduling in grid computing to decrease missed task, World academy of science, engineering and technology, 55, 2011, pp. 5–9.
[10]
S. Dhage, B. Meshram, R. Rawat, S. Padawe, M. Paingaokar, and A. Misra. Intrusion detection system in cloud computing environment. In Proc. of the Int. Conf. on Emerging Trends in Technology, 2011, pp. 235–249.
[11]
W. Xin, H. Ting-lei, and L. Xiao-yu. Research on the Intrusion detection mechanism based on cloud computing. In Proc. of the Int. Conf. on Intelligent Computing and Integrated Systems, 2010, pp. 125–138.
[12]
M. Colajanni, M. Marchetti, M. Messori. Selective and early threat detection in large networked systems. In 10th IEEE Int. Conf. on Computer and Information Technology, 2010, pp. 604–611.
[13]
M. Ficco and M. Rak. Intrusion tolerance as a service: A SLA-based solution. In Proc. of the 2nd Int. Conf. on Cloud Computing and Services Science, 2012, pp. 375–384. IEEE CS Press.
[14]
Chi-Chun Lo, Chun-Chieh Huang, and Joy Ku. A Cooperative Intrusion Detection System Framework for Cloud Computing Networks. In Proc. of the 39th Int. Conf. on Parallel Processing, 2010, pp. 280–284. IEEE CS Press.
[15]
M. Ficco. Security event correlation approach for cloud computing. In Journal of High Performance Computing and Networking, vol 7, no. 3, 2013.
[16]
HA. Kholidy, and F. Baiardi. CIDS: a Framework for Intrusion Detectionin Cloud Systems. In Proc. of the 9th Int. Conf. on Information Technology: New Generations, 2012, pp. 379–385.
[17]
M. Correia, N. F. Neves, L. Cheuk Lung, and P. Verssimo. Worm-IT - A wormhole-based intrusion tolerant group communication system. In Journal of Systems and Software archive, vol. 80, no. 2, Feb. 2007, pp. 178–197.
[18]
Prelude, an Hybrid Intrusion Detection System. Available at: http://www.prelude-ids.org [Last access: May, 2013].
