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New Challenges in Security, Privacy and Trust for Mobile Systems...
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New Challenges in Security, Privacy and Trust for Mobile Systems and Networks

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 (25 September 2024) | Viewed by 8152

Special Issue Editors

Dr. Wenjia Li

E-Mail Website
Guest Editor
Department of Computer Science, School of Engineering and Computing Sciences, New York Institute of Technology, New York, NY 10023, USA
Interests: security and trust; mobile and wireless systems; IoT; cyber physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Chen

E-Mail Website
Guest Editor
Department of Information Technology, College of Engineering and Computing, Georgia Southern University, P.O. BOX 8150, Statesboro, GA, USA
Interests: wireless networks; information security; digital forensics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yun Lin

E-Mail Website
Guest Editor
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: fault detection and recognition; machine learning and data analytics over wireless networks; signal processing and analysis; cognitive radio and software defined radio; artificial intelligence; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Security, trust, and privacy are critical concerns in mobile and wireless systems due to the widespread use of mobile devices and technology that utilizes the Internet of Things (IoT). The rapid development of these technologies has led to an increase in various security risks, making it essential to ensure that the data transmitted through these systems remains secure and private. Mobile and wireless systems face various security threats, such as unauthorized access, data breaches, and malware attacks. To address these concerns, measures such as encryption, authentication, and access control are used. Trust is also a crucial aspect of mobile and wireless systems, as users must have confidence in the system's security to use it. Privacy is another concern, as mobile devices often store and transmit sensitive data. Therefore, to protect privacy, techniques such as anonymization and data minimization are employed. This Special Issue welcomes original research and review articles that address aspects of security, trust and privacy in mobile systems and networks.

Dr. Wenjia Li
Dr. Lei Chen
Prof. Dr. Yun Lin
Guest Editors

Manuscript Submission Information

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Keywords

  • security
  • trust
  • privacy
  • mobile
  • wireless networks
  • IoT, Android

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Published Papers (4 papers)

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Research

21 pages, 706 KiB  
Article
SSCL-TransMD: Semi-Supervised Continual Learning Transformer for Malicious Software Detection
by Liang Kou, Donghui Zhao, Hui Han, Xiong Xu, Shuaige Gong and Liandong Wang
Appl. Sci. 2023, 13(22), 12255; https://doi.org/10.3390/app132212255 - 13 Nov 2023
Cited by 2 | Viewed by 1711
Abstract
Machine learning-based malware (malicious software) detection methods have a wide range of real-world applications. However, these types of approaches suffer from the fatal problem of “model aging”, in which the validity of the model decreases rapidly as the malware continues to evolve and [...] Read more.
Machine learning-based malware (malicious software) detection methods have a wide range of real-world applications. However, these types of approaches suffer from the fatal problem of “model aging”, in which the validity of the model decreases rapidly as the malware continues to evolve and variants emerge continuously. The model aging problem is usually solved by model retraining, which relies on lots of labeled samples obtained at great expense. To address this challenge, this paper proposes a semi-supervised continuous learning malware detection model based on Transformer. Firstly, this model improves the lifelong semi-supervised mixture algorithm to dynamically adjust the weighted combination of new sample sequences and historical ones to solve the imbalance problem. Secondly, the Learning with Local and Global Consistency algorithm is used to iteratively compute similarity scores for the unlabeled samples in the mixed samples to obtain pseudo-labels. Lastly, the Multilayer Perceptron is applied for malware classification. To validate the effectiveness of the model, this paper conducts experiments on the CICMalDroid2020 dataset. The experimental results show that the proposed model performs better than existing deep learning detection models. The F1 score has an average improvement of 1.27% compared to other models when conducting binary classification. And, after inputting hybrid samples, including historical data and new data, four times, the F1 score is still 1.96% higher than other models. Full article
(This article belongs to the Special Issue New Challenges in Security, Privacy and Trust for Mobile Systems and Networks)
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22 pages, 3057 KiB  
Article
Adversarial Example Detection and Restoration Defensive Framework for Signal Intelligent Recognition Networks
by Chao Han, Ruoxi Qin, Linyuan Wang, Weijia Cui, Dongyang Li and Bin Yan
Appl. Sci. 2023, 13(21), 11880; https://doi.org/10.3390/app132111880 - 30 Oct 2023
Viewed by 1779
Abstract
Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques. Our design features a detection AE that harnesses reconstruction errors and convolutional neural networks to [...] Read more.
Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques. Our design features a detection AE that harnesses reconstruction errors and convolutional neural networks to discern deep features, employing thresholds from reconstruction error and Kullback–Leibler divergence to identify adversarial samples and their origin mechanisms. Additionally, a restoration AE with a multi-layered structure effectively restores adversarial samples generated via optimization methods, ensuring accurate classification. Tested rigorously on the RML2016.10a dataset, our framework proves robust against adversarial threats, presenting a versatile defense solution compatible with various deep learning models. Full article
(This article belongs to the Special Issue New Challenges in Security, Privacy and Trust for Mobile Systems and Networks)
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20 pages, 3758 KiB  
Article
Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network
by Omar I. Dallal Bashi, Shymaa Mohammed Jameel, Yasir Mahmood Al Kubaisi, Husamuldeen K. Hameed and Ahmad H. Sabry
Appl. Sci. 2023, 13(20), 11592; https://doi.org/10.3390/app132011592 - 23 Oct 2023
Cited by 2 | Viewed by 1867
Abstract
Security identification solutions against WLAN network attacks according to straightforward digital detectors, such as SSID, IP addresses, and MAC addresses, are not efficient in identifying such hacking or router impersonation. These detectors can be simply mocked. Therefore, a further protected key uses new [...] Read more.
Security identification solutions against WLAN network attacks according to straightforward digital detectors, such as SSID, IP addresses, and MAC addresses, are not efficient in identifying such hacking or router impersonation. These detectors can be simply mocked. Therefore, a further protected key uses new information by combining these simple digital identifiers with an RF signature of the radio link. In this work, a design of a convolutional neural network (CNN) based on fingerprinting radio frequency (RF) is developed with computer-generated data. The developed CNN is trained with beacon frames of a wireless local area network (WLAN) that is simulated as a result of identified and unidentified router nodes of fingerprinting RF. The proposed CNN is able to detect router impersonators by comparing the data pair of the MAC address and RF signature of the received signal from the known and unknown routers. ADAM optimizer, which is the extended version of stochastic gradient descent, has been used with a developed deep learning convolutional neural network containing three fully connected and two convolutional layers. According to the training progress graphic, the network converges to around 100% accuracy within the first epoch, which indicates that the developed architecture was efficient in detecting router impersonations. Full article
(This article belongs to the Special Issue New Challenges in Security, Privacy and Trust for Mobile Systems and Networks)
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15 pages, 4582 KiB  
Article
A Novel DFA on AES: Based on Two–Byte Fault Model with Discontiguous Rows
by Xusen Wan, Jinbao Zhang, Shi Cheng, Weixiang Wu and Jiehua Wang
Appl. Sci. 2023, 13(14), 8282; https://doi.org/10.3390/app13148282 - 18 Jul 2023
Cited by 1 | Viewed by 1465
Abstract
Differential fault attack (DFA) is a distinctive methodology for acquiring the key to block ciphers, which comprises two distinct strategies: DFA on the state and DFA on the key schedule. Given the widespread adoption of the Advanced Encryption Standard (AES), it has emerged [...] Read more.
Differential fault attack (DFA) is a distinctive methodology for acquiring the key to block ciphers, which comprises two distinct strategies: DFA on the state and DFA on the key schedule. Given the widespread adoption of the Advanced Encryption Standard (AES), it has emerged as a prominent target for DFA. This paper presents an efficient DFA on the AES, utilizing a two−byte fault model that induces faults at the state with discontiguous rows. The experiment demonstrates that, based on the proposed fault model, the key for AES–128, AES–192, and AES–256 can be successfully recovered by exploiting two, two, and four faults, respectively, without the need for exhaustive research. Notably, in the case of AES–256, when considering exhaustive research, two (or three) faults are needed with 232 (or 216) exhaustive searches. In comparison to the currently available DFA on the AES state, the proposed attack method shows a higher efficiency due to the reduced induced faults. Full article
(This article belongs to the Special Issue New Challenges in Security, Privacy and Trust for Mobile Systems and Networks)
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