# Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach

arXiv: https://arxiv.org/abs/1712.05914 · HTML: https://mlsanity.com/paper/1712.05914/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [Artificial Neural Network for Cybersecurity: A Comprehensive Review](https://mlsanity.com/paper/2107.01185/) (2021)
- [NLP-Powered Repository and Search Engine for Academic Papers: A Case Study on Cyber Risk Literature with CyLit](https://mlsanity.com/paper/2409.06226/) (2024)
- [Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks](https://mlsanity.com/paper/2112.00988/) (2021)

## Abstract (arXiv)

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.