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Towards Performing Classification in Cloud Forensics with Self Organizing Maps


Sugandh Bhatia, Jyoteesh Malhotra


Vol. 20  No. 8  pp. 94-103


======= DOI: 10.22937/IJCSNS.2020.20.08.9 ======= In the recent years, cloud computing applications and services have witnessed the exponential growth in every sphere of Government, non-government organizations and corporate houses. Security, privacy, compliance, SLA, integrity and availability are the key issues in cloud computing. This research article concentrates on the investigation and analysis by implementing self organizing maps. The investigation is conducted on a set of file systems such as internet operations, email and exif data across two forensic cases named as SEL01 and SEL02. Outcome of the analysis divulged that self organizing maps can be executed as a significant unsupervised clustering tool in digital forensics. Hence, an effort has been made to apply various tools and techniques to extract proof and evidence from different sets of data and on the basis of output it is feasible to find out the percentage of significant and insignificant files.


cloud forensics, self organizing maps, machine learning, cluster, significant