Enhancing Data Security in the User Layer of Mobile Cloud Computing Environment: A Novel Approach

TL;DR

This paper proposes MINDPRES, a novel intrusion detection and prevention system that combines host-based and network-based IDS with machine learning to improve security at the user layer in mobile cloud computing environments.

Contribution

It introduces a new approach that integrates host and network IDS with machine learning for enhanced security in MCC user layers.

Findings

Preliminary results suggest improved detection of malicious activities.

The approach effectively analyzes device resources and network traffic.

Future work includes development and evaluation across mobile platforms.

Abstract

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.