Enhancing Security-Problem-Based Deep Learning in Mobile Edge Computing

Xiao Zheng, Mingchu Li, Syed Bilal Hussain Shah, Dinh Thuan Do, Yuanfang Chen, Constandinos X. Mavromoustakis, George Mastorakis, Evangelos Pallis

Research output: Contribution to journalArticlepeer-review


The implementation of a variety of complex and energy-intensive mobile applications by resource-limited mobile devices (MDs) is a huge challenge. Fortunately, mobile edge computing (MEC) as a new computing paragon can offer rich resources to perform all or part of the MD's task, which greatly reduces the energy consumption of the MD and improves the quality of service (QoS) for applications. However, offloading tasks to the edge server is vulnerable to attacks such as tampering and snooping, resulting in a deep learning (DL) security feature developed by major cloud service providers. An effective security strategy method to minimize ongoing attacks in the MEC setting is proposed. The algorithm is based on the synthetic principle of a special set of strategies, and it can quickly construct suboptimal solutions even if the number of targets achieves hundreds of millions. In addition, for a given structure and a given number of patrollers, the upper bound of the protection level can be obtained, and the lower bound required for a given protection level can also be inferred. These bounds apply to universal strategies. By comparing with the previous three basic experiments, it can be proved that our algorithm is better than the previous ones in terms of security and running time.

Original languageEnglish
Article number49
JournalACM Transactions on Internet Technology
Issue number2
Publication statusPublished - May 2022


  • deep learning
  • mobile device
  • Mobile edge computing
  • quality of service
  • security strategies
  • suboptimal
  • synthetic theories


Dive into the research topics of 'Enhancing Security-Problem-Based Deep Learning in Mobile Edge Computing'. Together they form a unique fingerprint.

Cite this