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Author Khan, Suleman ♦ Gani, Abdullah ♦ Wahab, Ainuddin Wahid Abdul ♦ Bagiwa, Mustapha Aminu ♦ Shiraz, Muhammad ♦ Khan, Samee U. ♦ Buyya, Rajkumar ♦ Zomaya, Albert Y.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Cloud computing ♦ Authenticity ♦ Big data ♦ Cloud log forensics ♦ Confidentiality ♦ Correlation of cloud logs ♦ Integrity
Abstract Cloud log forensics (CLF) mitigates the investigation process by identifying the malicious behavior of attackers through profound cloud log analysis. However, the accessibility attributes of cloud logs obstruct accomplishment of the goal to investigate cloud logs for various susceptibilities. Accessibility involves the issues of cloud log access, selection of proper cloud log file, cloud log data integrity, and trustworthiness of cloud logs. Therefore, forensic investigators of cloud log files are dependent on cloud service providers (CSPs) to get access of different cloud logs. Accessing cloud logs from outside the cloud without depending on the CSP is a challenging research area, whereas the increase in cloud attacks has increased the need for CLF to investigate the malicious activities of attackers. This paper reviews the state of the art of CLF and highlights different challenges and issues involved in investigating cloud log data. The logging mode, the importance of CLF, and cloud log-as-a-service are introduced. Moreover, case studies related to CLF are explained to highlight the practical implementation of cloud log investigation for analyzing malicious behaviors. The CLF security requirements, vulnerability points, and challenges are identified to tolerate different cloud log susceptibilities. We identify and introduce challenges and future directions to highlight open research areas of CLF for motivating investigators, academicians, and researchers to investigate them.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-05-12
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 49
Issue Number 1
Page Count 42
Starting Page 1
Ending Page 42


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Source: ACM Digital Library