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Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation

Submission Deadline: 30 April 2024 Submit to Special Issue

Guest Editors

Prof. Ilsun You, Kookmin University, South Korea
Prof. Xiaofeng Chen, Xidian University, China
Dr. Vishal Sharma, Queen's University Belfast (QUB), United Kingdom
Dr. Gaurav Choudhary, University of Southern Denmark, Denmark

Summary

Mobile internet technologies have transformed our daily lives, allowing us to connect, communicate, and access a wide range of services and applications anytime and anywhere. Moreover, mobile internet technologies are poised to play a key role in the upcoming era of great digital transformation, in synergy with future core technologies such as 6G, quantum computing, and generative AI. However, as we enter this new era, securing the mobile internet has become a critical challenge. We need to anticipate and address the new security issues and threats that will arise from the use of mobile internet technology in the era of great digital transformation.

 

The special issue invites researchers and practitioners to submit their original research papers, reviews, and case studies that contribute to the advanced security for the Future Mobile Internet Technologies in Digital Transformation era (FMIT-DT). The following non-exhaustive list of topics highlights the scope and interest of this special issue:

 

- Emerging Threats and Countermeasures in FMIT-DT

- Advances in Mobile Malware Detection and Prevention

- Privacy and Data Protection for FMIT-DT

- Authentication and Access Control mechanisms for FMIT-DT

- Secure Mobile Internet Protocols and Formal Verification for FMIT-DT

- Machine learning and AI-Driven Security Solutions for FMIT-DT

- Blockchain-based Security Mechanisms for FMIT-DT

- Mobile Device Management (MDM) and Mobile Security Policies

- Secure edge computing and fog computing in Future Mobile Internet Environment

- Quantum-resistant encryption and cryptography for FMIT-DT

- Trust and reputation management in FMIT-DT

- Advanced cyber threats in Future Mobile Internet Environment

- Mobile Device Management (MDM) and Mobile Security Policies


Keywords

future mobile internet security, 5GB/6G security, advanced security for the DX era, ML and AI for future mobile internet security, security protocol for future mobile internet

Published Papers


  • Open Access

    ARTICLE

    Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices

    So-Eun Jeon, Ye-Sol Oh, Yeon-Ji Lee, Il-Gu Lee
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.047239
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract With the advancement of wireless network technology, vast amounts of traffic have been generated, and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated. While signature-based detection methods, static analysis, and dynamic analysis techniques have been previously explored for malicious traffic detection, they have limitations in identifying diversified malware traffic patterns. Recent research has been focused on the application of machine learning to detect these patterns. However, applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process. In this study, we examined methods… More >

  • Open Access

    ARTICLE

    An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

    Younghoon Ban, Myeonghyun Kim, Haehyun Cho
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3535-3563, 2024, DOI:10.32604/cmes.2023.046658
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract Antivirus vendors and the research community employ Machine Learning (ML) or Deep Learning (DL)-based static analysis techniques for efficient identification of new threats, given the continual emergence of novel malware variants. On the other hand, numerous researchers have reported that Adversarial Examples (AEs), generated by manipulating previously detected malware, can successfully evade ML/DL-based classifiers. Commercial antivirus systems, in particular, have been identified as vulnerable to such AEs. This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers. Our attack method utilizes seven different perturbations, including Overlay Append, Section Append, and Break Checksum, capitalizing on the ambiguities present… More >

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