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Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing:...
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Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing: Challenges and Solutions for Edge Architectures

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 8428

Special Issue Editors

Dr. Mingxiong Zhao

E-Mail Website
Guest Editor
National Pilot School of Software, Yunnan University, Kunming 650091, China
Interests: network security; mobile edge computing; edge AI techniques
Dr. Di Liu

E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway
Interests: edge intelligence; embedded systems; real-time scheduling
Dr. Yunchun Zhang

E-Mail Website
Guest Editor
National Pilot School of Software, Yunnan University, Kunming 650091, China
Interests: network and cyberspace security; artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

Mobile Edge Computing (MEC) has emerged as a promising solution to meet the growing demand for low-latency and high-bandwidth applications and services such as augmented reality, autonomous vehicles, and smart cities. However, MEC nodes are often limited by computing resources, storage capacity, and energy resources, which makes them vulnerable to a range of malicious attacks such as DDoS attacks, malware, and privacy breaches. This, in turn, poses significant challenges in building a secure and robust MEC system that is capable of functioning effectively on edge architecture. As a result, there is a pressing need for research and development efforts to address the technical challenges ahead and ensure that MEC networks are resilient against various forms of cyber threats.

This Special Issue aims to gather original and high-quality research papers that present recent advances and innovative solutions in the security and defense of MEC. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Resource allocation for MEC networks
  • MEC security and privacy
  • MEC-based federated learning
  • MEC-enabled application security
  • MEC-based privacy protection
  • Edge intelligence and machine learning at the edge
  • MEC-oriented adversarial attacks and defense
  • Security and privacy issues in integrated sensing/communication networks
  • The design of security in edge paradigms
  • Privacy leakage, service manipulation, and rogue infrastructure against MEC infrastructure
  • Attacks targeting virtualization infrastructure
  • Injection of information and service manipulation by MEC devices
  • Protocol and network security in MEC
  • Trust management in MEC
  • Intrusion detection systems in MEC
  • Access control systems for MEC

We look forward to receiving your contributions.

Dr. Mingxiong Zhao
Dr. Di Liu
Dr. Yunchun Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MEC
  • privacy protection
  • edge paradigm
  • computing migration security

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Published Papers (6 papers)

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Research

20 pages, 6747 KiB  
Article
Meta-Hybrid: Integrate Meta-Learning to Enhance Class Imbalance Graph Learning
by Liming Ran, Hongyu Sun, Lanqi Gao, Yanhua Dong and Yang Lu
Electronics 2024, 13(18), 3769; https://doi.org/10.3390/electronics13183769 - 22 Sep 2024
Viewed by 920
Abstract
The class imbalance problem is a significant challenge in node classification tasks. Since majority class samples dominate imbalanced data, the model tends to favor the majority class, resulting in insufficient ability to identify minority classes. Evaluation indicators such as accuracy may not fully [...] Read more.
The class imbalance problem is a significant challenge in node classification tasks. Since majority class samples dominate imbalanced data, the model tends to favor the majority class, resulting in insufficient ability to identify minority classes. Evaluation indicators such as accuracy may not fully reflect the model’s performance. To solve these undesirable effects, we propose a framework for synthesizing minority class samples, GraphSHX, to balance the number of samples of different classes, and integrate the XGBoost model for node classification prediction during the training process. Conventional graph neural networks (GNNs) yielded unsatisfactory results, possibly due to the limited number of newly generated nodes. Therefore, we introduce a meta-mechanism to deal with small-sample problems, and employ the meta-learning approach to enhance performance on small-sample tasks by learning from a large number of tasks. An empirical evaluation of node classification on six publicly available datasets demonstrated that our balanced data set method outperforms existing optimal loss repair methods and synthetic node methods. The addition of the XGBoost model and meta-learning improves the accuracy by more than 5% to 10%, with the overall accuracy of the improved model being 15% higher than that of the baseline method. Full article
(This article belongs to the Special Issue Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing: Challenges and Solutions for Edge Architectures)
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14 pages, 3096 KiB  
Article
Research and Application of the Median Filtering Method in Enhancing the Imperceptibility of Perturbations in Adversarial Examples
by Yiming He, Yanhua Dong and Hongyu Sun
Electronics 2024, 13(13), 2458; https://doi.org/10.3390/electronics13132458 - 23 Jun 2024
Cited by 1 | Viewed by 899
Abstract
In the field of object detection, the adversarial attack method based on generative adversarial network efficiently generates adversarial examples, thereby significantly reducing time costs. However, this approach overlooks the imperceptibility of perturbations in adversarial examples, resulting in poor visual performance and insufficient invisibility [...] Read more.
In the field of object detection, the adversarial attack method based on generative adversarial network efficiently generates adversarial examples, thereby significantly reducing time costs. However, this approach overlooks the imperceptibility of perturbations in adversarial examples, resulting in poor visual performance and insufficient invisibility of the generated adversarial examples. To further enhance the imperceptibility of perturbations in adversarial examples, a method utilizing median filtering is proposed to address these generated perturbations. Experimental evaluations were conducted on the Pascal VOC dataset. The results demonstrate that, compared to the original image, there is an increase of at least 17.2% in the structural similarity index (SSIM) for generated adversarial examples. Additionally, the peak signal-to-noise ratio (PSNR) increases by at least 27.5%, while learned perceptual image patch similarity (LPIPS) decreases by at least 84.6%. These findings indicate that the perturbations in generated adversarial examples are more difficult to detect, with significantly improved imperceptibility and closer resemblance to the original image without compromising their high aggressiveness. Full article
(This article belongs to the Special Issue Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing: Challenges and Solutions for Edge Architectures)
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17 pages, 636 KiB  
Article
A Robust CNN for Malware Classification against Executable Adversarial Attack
by Yunchun Zhang, Jiaqi Jiang, Chao Yi, Hai Li, Shaohui Min, Ruifeng Zuo, Zhenzhou An and Yongtao Yu
Electronics 2024, 13(5), 989; https://doi.org/10.3390/electronics13050989 - 5 Mar 2024
Cited by 1 | Viewed by 1656
Abstract
Deep-learning-based malware-detection models are threatened by adversarial attacks. This paper designs a robust and secure convolutional neural network (CNN) for malware classification. First, three CNNs with different pooling layers, including global average pooling (GAP), global max pooling (GMP), and spatial pyramid pooling (SPP), [...] Read more.
Deep-learning-based malware-detection models are threatened by adversarial attacks. This paper designs a robust and secure convolutional neural network (CNN) for malware classification. First, three CNNs with different pooling layers, including global average pooling (GAP), global max pooling (GMP), and spatial pyramid pooling (SPP), are proposed. Second, we designed an executable adversarial attack to construct adversarial malware by changing the meaningless and unimportant segments within the Portable Executable (PE) header file. Finally, to consolidate the GMP-based CNN, a header-aware loss algorithm based on the attention mechanism is proposed to defend the executive adversarial attack. The experiments showed that the GMP-based CNN achieved better performance in malware detection than other CNNs with around 98.61% accuracy. However, all CNNs were vulnerable to the executable adversarial attack and a fast gradient-based attack with a 46.34% and 34.65% accuracy decline on average, respectively. Meanwhile, the improved header-aware CNN achieved the best performance with an evasion ratio of less than 5.0%. Full article
(This article belongs to the Special Issue Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing: Challenges and Solutions for Edge Architectures)
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17 pages, 3201 KiB  
Article
An Improved Gravitational Search Algorithm for Task Offloading in a Mobile Edge Computing Network with Task Priority
by Ling Xu, Yunpeng Liu, Bing Fan, Xiaorong Xu, Yiguo Mei and Wei Feng
Electronics 2024, 13(3), 540; https://doi.org/10.3390/electronics13030540 - 29 Jan 2024
Cited by 3 | Viewed by 1149
Abstract
Mobile edge computing (MEC) distributes computing and storage resources to the edge of the network closer to the user and significantly reduces user task completion latency and system energy consumption. This paper investigates the problem of computation offloading in a three-tier mobile edge [...] Read more.
Mobile edge computing (MEC) distributes computing and storage resources to the edge of the network closer to the user and significantly reduces user task completion latency and system energy consumption. This paper investigates the problem of computation offloading in a three-tier mobile edge computing network composed of multiple users, multiple edge servers, and a cloud server. In this network, each user’s task can be divided into multiple subtasks with serial and parallel priority relationships existing among these subtasks. An optimization model is established with the objective of minimizing the total user delay and processor cost under constraints such as the available resources of users and servers and the interrelationships among the subtasks. An improved gravitational search algorithm (IGSA) is proposed to solve this optimization model. In contrast with the other gravitational search algorithm, the convergence factor is introduced in the calculation of the resultant force and the crossover operation in a genetic algorithm is performed when generating the new particles during each iteration. The simulation results show that the proposed IGSA greatly improves the system performance compared with the existing algorithms. Full article
(This article belongs to the Special Issue Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing: Challenges and Solutions for Edge Architectures)
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21 pages, 1861 KiB  
Article
Learning-Based Collaborative Computation Offloading in UAV-Assisted Multi-Access Edge Computing
by Zikun Xu, Junhui Liu, Ying Guo, Yunyun Dong and Zhenli He
Electronics 2023, 12(20), 4371; https://doi.org/10.3390/electronics12204371 - 22 Oct 2023
Cited by 2 | Viewed by 1340
Abstract
Unmanned aerial vehicles (UAVs) have gained considerable attention in the research community due to their exceptional agility, maneuverability, and potential applications in fields like surveillance, multi-access edge computing (MEC), and various other domains. However, efficiently providing computation offloading services for concurrent Internet of [...] Read more.
Unmanned aerial vehicles (UAVs) have gained considerable attention in the research community due to their exceptional agility, maneuverability, and potential applications in fields like surveillance, multi-access edge computing (MEC), and various other domains. However, efficiently providing computation offloading services for concurrent Internet of Things devices (IOTDs) remains a significant challenge for UAVs due to their limited computing and communication capabilities. Consequently, optimizing and managing the constrained computing, communication, and energy resources of UAVs are essential for establishing an efficient aerial network infrastructure. To address this challenge, we investigate the collaborative computation offloading optimization problem in a UAV-assisted MEC environment comprising multiple UAVs and multiple IODTs. Our primary objective is to obtain efficient offloading strategies within a multi-heterogeneous UAV environment characterized by limited computing and communication capabilities. In this context, we model the problem as a multi-agent markov decision process (MAMDP) to account for environmental dynamics. We employ a multi-agent deep deterministic policy gradient (MADDPG) approach for task offloading. Subsequently, we conduct simulations to evaluate the efficiency of our proposed offloading scheme. The results highlight significant improvements achieved by the proposed offloading strategy, including a notable increase in the system completion rate and a significant reduction in the average energy consumption of the system. Full article
(This article belongs to the Special Issue Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing: Challenges and Solutions for Edge Architectures)
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22 pages, 67283 KiB  
Article
Binary-Convolution Data-Reduction Network for Edge–Cloud IIoT Anomaly Detection
by Cheng Xie, Wenbiao Tao, Zuoying Zeng and Yuran Dong
Electronics 2023, 12(15), 3229; https://doi.org/10.3390/electronics12153229 - 26 Jul 2023
Cited by 1 | Viewed by 1334
Abstract
Industrial anomaly detection, which relies on the analysis of industrial internet of things (IIoT) sensor data, is a critical element for guaranteeing the quality and safety of industrial manufacturing. Current solutions normally apply edge–cloud IIoT architecture. The edge side collects sensor data in [...] Read more.
Industrial anomaly detection, which relies on the analysis of industrial internet of things (IIoT) sensor data, is a critical element for guaranteeing the quality and safety of industrial manufacturing. Current solutions normally apply edge–cloud IIoT architecture. The edge side collects sensor data in the field, while the cloud side receives sensor data and analyzes anomalies to accomplish it. The more complete the data sent to the cloud side, the higher the anomaly-detection accuracy that can be achieved. However, it will be extremely expensive to collect all sensor data and transmit them to the cloud side due to the massive amounts and distributed deployments of IIoT sensors requiring expensive network traffics and computational capacities. Thus, it becomes a trade-off problem: “How to reduce data transmission under the premise of ensuring the accuracy of anomaly detection?”. To this end, the paper proposes a binary-convolution data-reduction network for edge–cloud IIoT anomaly detection. It collects raw sensor data and extracts their features at the edge side, and receives data features to discover anomalies at the cloud side. To implement this, a time-scalar binary feature encoder is proposed and deployed on the edge side, encoding raw data into time-series binary vectors. Then, a binary-convolution data-reduction network is presented at the edge side to extract data features that significantly reduce the data size without losing critical information. At last, a real-time anomaly detector based on hierarchical temporal memory (HTM) is established on the cloud side to identify anomalies. The proposed model is validated on the NAB dataset, and achieves 70.0, 64.6 and 74.0 on the three evaluation metrics of SP, RLFP and RLFN, while obtaining a reduction rate of 96.19%. Extensive experimental results demonstrate that the proposed method achieves new state-of-the-art results in anomaly detection with data reduction. The proposed method is also deployed on a real-world industrial project as a case study to prove the feasibility and effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing: Challenges and Solutions for Edge Architectures)
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