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Applied Sciences
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Data Security and Privacy in Mobile Cloud Computing
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Data Security and Privacy in Mobile Cloud Computing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4751

Special Issue Editors

Dr. Azeem Irshad

E-Mail Website1 Website2
Guest Editor
Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan
Interests: cryptography; security and privacy; attacks; distributed cloud computing; edge computing; RFID attacks; securing smart logistics; security in IoT; securing UAVs and intelligent vehciles
Dr. Muhammad Shafiq

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: Internet of Things; vehicle-to-everything communication; smart cities; machine learning, computational intelligence; data science; human factors engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Mourade Azrour

E-Mail Website
Guest Editor
Department of Computer, Faculty of Sciences and Technologies, Moulay Ismail University of Meknès, Errachidia 52000, Morocco
Interests: security; network; protocol
Special Issues, Collections and Topics in MDPI journals
Dr. Shehzad Ashraf Chaudhry

E-Mail Website
Guest Editor
Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
Interests: cryptography; Internet of Things; authentication; authenticated encryption; blockchains; 6G communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mobile cloud computing (MCC) refers to the accessibility of cloud computing services in a mobile environment. The secure provisioning of cloud services to mobile devices in the form of dependable computation and reliable storage requires more vigorous methods and techniques. This SI aims to elicit high-quality research articles on the security and privacy of MCC.

Recent computing frameworks have evolved swiftly owing to the unusual advancements in communication technology, including Internet of Things, edge computing, mobile cloud computing (MCC), blockchain, big data, the metaverse, etc. The MCC, with the help of service-oriented techniques, has largely changed the landscape of conventional cloud computing by offering on-demand mobile services and performing complex computations on resource-limited platforms. Although MCC offers considerable gains, including extensive storage, improved battery life, adaptability, consistency, and dependability, many challenges still hamper its adoption, such as energy efficiency, bandwidth, synchronization, data management, and security and privacy. Security has become a major concern in mobile cloud computing, as the service-rendering servers are scattered around the globe and handle data of a sensitive nature.

Authentication and privacy constitute fundamental security components to prevent unauthorized access for mobile cloud services in the MCC environment. While considerable focus has been placed on countering security and privacy issues in mobile computing, many security-related queries in the MCC setting have not been properly addressed, remaining unresolved and in need of more research. In the wake of terrific proliferation in the applications of IoT, cyber physical systems (CPS), mobile smart devices, IoT-enabled vehicles, and unmanned aerial vehicles (UAVs), we are bound to witness a surge in the number of security and privacy issues. Hence, there is a pressing need to design secure methods and techniques to counter a new generation of MCC-based cyber attacks posing serious threats to cloud-based systems.

The objective of this Special Issue is to highlight the state-of-the-art security and privacy issues amid solutions, with a particular focus on mobile cloud computing. Authors are encouraged to submit applied and theoretical approaches to handle these problems. Topics include:

  • The security and privacy of MCC.
  • Intrusion detection systems for MCC.
  • The security of mobile, peer-to-peer, and pervasive services in MCC.
  • The security of mobile commerce and mobile IoT.
  • The security of middleware support for MCC.
  • The security of pricing and billing for MCC-based services.
  • Privacy-enhancing frameworks for new computing environments.
  • Partitioning and offloading security in MCC.
  • Data and location privacy in MCC.
  • MCC-based reliable pricing and billing models.
  • MCC support for VANETS and ad hoc networks.
  • Identity and access management for mobile devices in MCC.
  • Securing cloudlet-based computing.
  • Lightweight authentication mechanisms in MCC architecture.
  • Blockchain-based security enhancement for distributed MCC.
  • Access control models in MCC.

Dr. Azeem Irshad
Dr. Muhammad Shafiq
Dr. Mourade Azrour
Dr. Shehzad Ashraf Chaudhry
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. Applied Sciences 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

  • security and privacy
  • mobile cloud computing
  • intrusion detection system
  • access control
  • cyber security
  • big data security
  • IoT security

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Published Papers (2 papers)

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Research

19 pages, 680 KiB  
Article
Intrusion Detection for Industrial Control Systems Based on Improved Contrastive Learning SimCLR
by Chengcheng Li, Fei Li, Liyan Zhang, Aimin Yang, Zhibin Hu and Ming He
Appl. Sci. 2023, 13(16), 9227; https://doi.org/10.3390/app13169227 - 14 Aug 2023
Cited by 1 | Viewed by 1598
Abstract
Since supervised learning intrusion detection models rely on manually labeled data, the process often requires a lot of time and effort. To make full use of unlabeled network traffic data and improve intrusion detection, this paper proposes an intrusion detection method for industrial [...] Read more.
Since supervised learning intrusion detection models rely on manually labeled data, the process often requires a lot of time and effort. To make full use of unlabeled network traffic data and improve intrusion detection, this paper proposes an intrusion detection method for industrial control systems based on improved comparative learning SimCLR. Firstly, a feature extraction network is trained on SimCLR using unlabeled data; a linear classification layer is added to the trained feature extraction network model; and a small amount of labeled data is used for supervised training and fine-tuning of the model parameters. The trained model is simulated on the Secure Water Treatment (SWaT) dataset and the publicly available industrial control dataset from Mississippi State University, and the results show that the method has better results in all evaluation metrics compared with the deep learning algorithm using supervised learning directly, and the comparative learning has research value in industrial control system intrusion detection. Full article
(This article belongs to the Special Issue Data Security and Privacy in Mobile Cloud Computing)
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21 pages, 9537 KiB  
Article
FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
by Sara Amaouche, Azidine Guezzaz, Said Benkirane, Mourade Azrour, Sohaib Bin Altaf Khattak, Haleem Farman and Moustafa M. Nasralla
Appl. Sci. 2023, 13(13), 7488; https://doi.org/10.3390/app13137488 - 25 Jun 2023
Cited by 19 | Viewed by 1737
Abstract
Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles [...] Read more.
Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles are interconnected through a wireless medium, making the network susceptible to various attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), or even black hole attacks that exploit the wireless medium to disrupt the network. These attacks degrade the network performance of VANETs and prevent legitimate users from accessing resources. VANETs face unique challenges due to the fast mobility of vehicles and dynamic changes in network topology. The high-speed movement of vehicles results in frequent alterations in the network structure, posing difficulties in establishing and maintaining stable communication. Moreover, the dynamic nature of VANETs, with vehicles joining and leaving the network regularly, adds complexity to implementing effective security measures. These inherent constraints necessitate the development of robust and efficient solutions tailored to VANETs, ensuring secure and reliable communication in dynamic and rapidly evolving environments. Therefore, securing communication in VANETs is a crucial requirement. Traditional security countermeasures are not pertinent to autonomous vehicles. However, many machine learning (ML) technologies are being utilized to classify malicious packet information and a variety of solutions have been suggested to improve security in VANETs. In this paper, we propose an enhanced intrusion detection framework for VANETs that leverages mutual information to select the most relevant features for building an effective model and synthetic minority oversampling (SMOTE) to deal with the class imbalance problem. Random Forest (RF) is applied as our classifier, and the proposed method is compared with different ML techniques such as logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), and Support Vector Machine (SVM). The model is tested on three datasets, namely ToN-IoT, NSL-KDD, and CICIDS2017, addressing challenges such as missing values, unbalanced data, and categorical values. Our model demonstrated great performance in comparison to other models. It achieved high accuracy, precision, recall, and f1 score, with a 100% accuracy rate on the ToN-IoT dataset and 99.9% on both NSL-KDD and CICIDS2017 datasets. Furthermore, the ROC curve analysis demonstrated our model’s exceptional performance, achieving a 100% AUC score. Full article
(This article belongs to the Special Issue Data Security and Privacy in Mobile Cloud Computing)
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