Enhancing Data Security in the User Layer of Mobile Cloud Computing Environment: A Novel Approach
This paper addresses the problem of enhancing data security in the User Layer of the Mobile Cloud Computing environment, which is an incremental improvement to existing IDS solutions.
This paper reviews existing Intrusion Detection Systems (IDS) for Mobile Cloud Computing (MCC), Cloud Computing (CC), and Mobile Device (MD) environments, identifying drawbacks. It proposes MINDPRES, a novel approach combining host-based and network-based IDS with Machine Learning (ML) for dynamic analysis of device resources and network traffic to detect malicious activities in the User Layer (UL) of MCC.
This paper reviews existing Intrusion Detection Systems (IDS) that target the Mobile Cloud Computing (MCC), Cloud Computing (CC), and Mobile Device (MD) environment. The review identifies the drawbacks in existing solutions and proposes a novel approach towards enhancing the security of the User Layer (UL) in the MCC environment. The approach named MINDPRES (Mobile- Cloud Intrusion Detection and Prevention System) combines a host-based IDS and network-based IDS using Machine Learning (ML) techniques. It applies dynamic analysis of both device resources and network traffic in order to detect malicious activities at the UL in the MCC environment. Preliminary investigations show that our approach will enhance the security of the UL in the MCC environment. Our future work will include the development and the evaluation of the proposed model across the various mobile platforms in the MCC environment.