Correlation‐based sequence alignment models for detecting masquerades in cloud computing

Author:

Kholidy Hisham A.1

Affiliation:

1. Department of Network and Computer Security (NCS)College of Engineering, State University of New York (SUNY) Polytechnic Institute100 Seymour RoadUticaNYUSA

Publisher

Institution of Engineering and Technology (IET)

Subject

Computer Networks and Communications,Information Systems,Software

Reference41 articles.

1. ‘Security guidance for critical areas of focus in cloud computing v4.0’ Cloud Security Alliance 2017. Available athttps://downloads.cloudsecurityalliance.org/assets/research/security‐guidance/security‐guidance‐v4‐FINAL.pdf

2. C. S. Alliance: ‘Top threats to cloud computing’ 2018. Available athttps://cloudsecurityalliance.org/articles/csa‐releases‐top‐threats‐to‐cloud‐computing‐deep‐dive/

3. Deep learning approaches for predictive masquerade detection;Elmasry W.;Secur. Commun. Netw.,2018

4. Sequence alignment for masquerade detection

5. Berlin K. Slater D. Saxe J.: ‘Malicious behavior detection using windows audit logs’.Proc. of the 8th ACM Workshop on Artificial Intelligence and Security (AISec 2015) Denver CO USA 2015 pp.35–44 https://doi.org/10.1145/2808769.2808773

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