Title :
Toward a Real-Time Cloud Auditing Paradigm
Author :
Nix, Robert ; Kantarcioglu, Murat ; Shetty, Sachin
Author_Institution :
Sch. of Comput. & Inf., Lipscomb Univ., Nashville, TN, USA
fDate :
June 28 2013-July 3 2013
Abstract :
The amount of computing done in the cloud is greatly increasing. The decentralized nature of the cloud, however, makes it difficult for individuals to ensure that the computation is being done correctly. Thus, the concept of "cloud auditing" has appeared. As applications in the cloud become more sensitive, the need for auditing systems to provide rapid analysis and quick responses also increases. Machine learning algorithms can be employed for the purposes of providing audit data. Few of these algorithms can be done in an online fashion, however. In this work, we examine one such online machine learning algorithm, and describe how it might be employed in a distributed computing environment.
Keywords :
auditing; cloud computing; learning (artificial intelligence); audit data; decentralized nature; distributed computing environment; online machine learning algorithm; real-time cloud auditing computation paradigm; Computational modeling; Data mining; Fasteners; Machine learning algorithms; Real-time systems; Storms; Topology; cloud auditing; machine learning; real time processing; security;
Conference_Titel :
Services (SERVICES), 2013 IEEE Ninth World Congress on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5024-4
DOI :
10.1109/SERVICES.2013.43