Cloud forensic frameworks based on machine learning techniques

Abstract

Cloud computing is considered to be one of the most significant and influential topics in the field of computing sciences. With time, cloud computing has paved its way in almost every aspect of human life. With the significant hike in number of users and service provider in the cloud environment, attackers are also increasing malicious activities in this area. Due to criticality in the area of the cloud computing, it is necessary that cloud environment should be safe. The concept of Cloud forensics has been introduced to establish a well-defined forensic capability in cloud environment. Although a lot of work has been carried out in the area of cloud forensic challenges and solutions, but the research on its frameworks and methodologies is still to be explored. The major challenge lies in providing a framework for analysis of massive amounts of forensic data in limited period. As proposed by many researchers, one of the best solutions for such analysis is the use of machine learning methods. This paper provides the study on the methodological aspect of cloud forensic analysis using various machine learning approaches. It gives a critical review of existing cloud forensic methodologies making use of machine learning for investigation of security related incidents in cloud. Furthermore, it provides a comprehensive study and comparison of existing frameworks using machine learning for digital and cloud forensic analysis, their drawbacks and scope for novel future research directions in thisarea.