In this paper detection method for the illegal
access to the cloud infrastructure is proposed. Detection process is based on
the collaborative filtering algorithm constructed on the cloud model. Here,
first of all, the normal behavior of the user is formed in the shape of a cloud
model, then these models are compared with each other by using the cosine
similarity method and by applying the collaborative filtering method the
deviations from the normal behavior are evaluated. If the deviation value is
above than the threshold, the user who gained access to the system is evaluated
as illegal, otherwise he is evaluated as a real user.
Cite this paper
Alguliev, R. and Abdullaeva, F. (2014) Illegal Access Detection in the Cloud Computing Environment. Journal of Information Security
, 65-71. doi: 10.4236/jis.2014.52007
 Cloud Security Alliance (2010) Top Threats to Cloud Computing.
 Salem, M.B. and Stolfo, S.J. (2011) Data Collection and Analysis for Masquerade Attack Detection: Challenges and Lessons Learned. Columbia University Computer Science Technical Reports, Columbia University, 8 p.
 (2010) 2010 Cybersecurity Watch Survey: Cybercrime Increasing Faster than Some Company Defenses. CERT, 17 p.
 Arrington, M. (2009) In Our Inbox: Hundreds of Confidential Twitter Documents.
 Takahashi, D. (2010) French Hacker Who Leaked Twitter Documents to TechCrunch Is Busted.
 Danchev, D. (2009) ZDNET: French Hacker Gains Access to Twitter’s Admin Panel.
 Allen, P. (2010) Obama’s Twitter Password Revealed after French Hacker Arrested for Breaking into U.S. President’s Accoun.
 Lane, T. and Brodley, C.E. (1997) Sequence Matching and Learning in Anomaly Detection for Computer Security. Proceedings of the AAAI Workshop on AI Approaches to Fraud Detection and Risk Management, AAAI Press, 43-49.
 Alguliev, R.M. and Abdullayeva, F.C. (2013) Identity Management Based Security Architecture of Cloud Computing on Multi-Agent Systems. Proceedings of the Third International Conference on Innovative Computing Technology, London, 29-31 August, 123-126. http://dx.doi.org/10.1109/INTECH.2013.6653643
 Eliquliyev, R.M. and Abdullayeva, F.C. (2013) Bulud texnologiyalarenen tehlükesizlik problemlerinin tedqiqi ve analizi. Informasiya Texnologiyalare Problemleri, 1, 3-14.
 Coull, S.E., Branch, J., Szymanski, B. and Breimer, E. (2003) Intrusion Detection: A Bioinformatics Approach. Proceedings of the 19th Annual Computer Security Applications Conference, Las Vegas, 8-12 December 24-33.
 Coull, S.E. and Szymanski, B.K. (2008) Sequence Alignment for Masquerade Detection. Computational Statistics and Data Analysis, 52, 4116-4131. http://dx.doi.org/10.1016/j.csda.2008.01.022
 Stolfo, S.J., Salem, M.B. and Keromytis, A.D. (2012) Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud. Proceedings of the IEEE Symposium on Security and Privacy Workshops, San Francisco, 125-128.
 Greenberg, S. (1988) Using Unix: Collected Traces of 168 Users. Report, University of Calgary.
 Lane, T. and Brodley, C.E. (1997) An Application of Machine Learning to Anomaly Detection. Proceedings of the 20th National Information Systems Security Conference, 14 February, 366-380.
 Salem, M.B. and Stolfo, S.J. (2011) Modeling User Search Behavior for Masquerade Detection. Proceedings of the 14th International Conference on Recent Advances in Intrusion Detection, 181-200.