Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach

TL;DR

This paper introduces a deep learning framework for detecting cyberattacks in mobile cloud computing, achieving high accuracy and outperforming existing machine learning methods.

Contribution

It presents a novel deep learning-based framework specifically designed for cyberattack detection in mobile cloud environments, demonstrating superior accuracy.

Findings

Achieves up to 97.11% detection accuracy.

Recognizes diverse cyberattacks effectively.

Outperforms current machine learning approaches.

Abstract

With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution.