Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach
This addresses security issues like data integrity and service availability for mobile cloud applications, but it appears incremental as it builds on existing machine learning approaches.
The paper tackles the problem of detecting cyberattacks in mobile cloud computing by proposing a deep learning framework, achieving up to 97.11% accuracy in attack detection.
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.