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

Submission Deadline: 30 April 2024 (closed) View: 769

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

    EDITORIAL

    Introduction to the Special Issue on Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation

    Ilsun You, Xiaofeng Chen, Vishal Sharma, Gaurav Choudhary
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1907-1909, 2024, DOI:10.32604/cmes.2024.058939
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Practical Privacy-Preserving ROI Encryption System for Surveillance Videos Supporting Selective Decryption

    Chan Hyeong Cho, Hyun Min Song, Taek-Young Youn
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1911-1931, 2024, DOI:10.32604/cmes.2024.053430
    (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 video recording devices and network infrastructure, we use surveillance cameras to protect our valuable assets. This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security. The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest (ROIs) within the video, linking these ROIs to generate unique IDs. These IDs are then combined with a master key to create entity-specific keys, which are used to encrypt the ROIs within the video. This system supports selective decryption, effectively protecting personal information More >

  • Open Access

    ARTICLE

    The Machine Learning Ensemble for Analyzing Internet of Things Networks: Botnet Detection and Device Identification

    Seung-Ju Han, Seong-Su Yoon, Ieck-Chae Euom
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1495-1518, 2024, DOI:10.32604/cmes.2024.053457
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract The rapid proliferation of Internet of Things (IoT) technology has facilitated automation across various sectors. Nevertheless, this advancement has also resulted in a notable surge in cyberattacks, notably botnets. As a result, research on network analysis has become vital. Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods. In this paper, we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework. The results indicate that using the More >

  • Open Access

    ARTICLE

    Encrypted Cyberattack Detection System over Encrypted IoT Traffic Based on Statistical Intelligence

    Il Hwan Ji, Ju Hyeon Lee, Seungho Jeon, Jung Taek Seo
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1519-1549, 2024, DOI:10.32604/cmes.2024.053437
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract In the early days of IoT’s introduction, it was challenging to introduce encryption communication due to the lack of performance of each component, such as computing resources like CPUs and batteries, to encrypt and decrypt data. Because IoT is applied and utilized in many important fields, a cyberattack on IoT can result in astronomical financial and human casualties. For this reason, the application of encrypted communication to IoT has been required, and the application of encrypted communication to IoT has become possible due to improvements in the computing performance of IoT devices and the development… More >

  • Open Access

    ARTICLE

    IWTW: A Framework for IoWT Cyber Threat Analysis

    GyuHyun Jeon, Hojun Jin, Ju Hyeon Lee, Seungho Jeon, Jung Taek Seo
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1575-1622, 2024, DOI:10.32604/cmes.2024.053465
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract The Internet of Wearable Things (IoWT) or Wearable Internet of Things (WIoT) is a new paradigm that combines IoT and wearable technology. Advances in IoT technology have enabled the miniaturization of sensors embedded in wearable devices and the ability to communicate data and access real-time information over low-power mobile networks. IoWT devices are highly interdependent with mobile devices. However, due to their limited processing power and bandwidth, IoWT devices are vulnerable to cyberattacks due to their low level of security. Threat modeling and frameworks for analyzing cyber threats against existing IoT or low-power protocols have… More >

  • Open Access

    ARTICLE

    Advancing 5G Network Applications Lifecycle Security: An ML-Driven Approach

    Ana Hermosilla, Jorge Gallego-Madrid, Pedro Martinez-Julia, Jordi Ortiz, Ved P. Kafle, Antonio Skarmeta
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1447-1471, 2024, DOI:10.32604/cmes.2024.053379
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract As 5th Generation (5G) and Beyond 5G (B5G) networks become increasingly prevalent, ensuring not only network security but also the security and reliability of the applications, the so-called network applications, becomes of paramount importance. This paper introduces a novel integrated model architecture, combining a network application validation framework with an AI-driven reactive system to enhance security in real-time. The proposed model leverages machine learning (ML) and artificial intelligence (AI) to dynamically monitor and respond to security threats, effectively mitigating potential risks before they impact the network infrastructure. This dual approach not only validates the functionality… More >

  • Open Access

    ARTICLE

    Multi-Binary Classifiers Using Optimal Feature Selection for Memory-Saving Intrusion Detection Systems

    Ye-Seul Kil, Yu-Ran Jeon, Sun-Jin Lee, Il-Gu Lee
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1473-1493, 2024, DOI:10.32604/cmes.2024.052637
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract With the rise of remote work and the digital industry, advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics, rendering them difficult to detect with conventional intrusion detection methods. Signature-based intrusion detection methods can be used to detect attacks; however, they cannot detect new malware. Endpoint detection and response (EDR) tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques. However, EDR tools are restricted by the continuous generation of unnecessary logs, resulting in poor detection… More >

  • Open Access

    ARTICLE

    Optimal Cyber Attack Strategy Using Reinforcement Learning Based on Common Vulnerability Scoring System

    Bum-Sok Kim, Hye-Won Suk, Yong-Hoon Choi, Dae-Sung Moon, Min-Suk Kim
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1551-1574, 2024, DOI:10.32604/cmes.2024.052375
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract Currently, cybersecurity threats such as data breaches and phishing have been on the rise due to the many different attack strategies of cyber attackers, significantly increasing risks to individuals and organizations. Traditional security technologies such as intrusion detection have been developed to respond to these cyber threats. Recently, advanced integrated cybersecurity that incorporates Artificial Intelligence has been the focus. In this paper, we propose a response strategy using a reinforcement-learning-based cyber-attack-defense simulation tool to address continuously evolving cyber threats. Additionally, we have implemented an effective reinforcement-learning-based cyber-attack scenario using Cyber Battle Simulation, which is a… More >

  • Open Access

    ARTICLE

    FedAdaSS: Federated Learning with Adaptive Parameter Server Selection Based on Elastic Cloud Resources

    Yuwei Xu, Baokang Zhao, Huan Zhou, Jinshu Su
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 609-629, 2024, DOI:10.32604/cmes.2024.053462
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract The rapid expansion of artificial intelligence (AI) applications has raised significant concerns about user privacy, prompting the development of privacy-preserving machine learning (ML) paradigms such as federated learning (FL). FL enables the distributed training of ML models, keeping data on local devices and thus addressing the privacy concerns of users. However, challenges arise from the heterogeneous nature of mobile client devices, partial engagement of training, and non-independent identically distributed (non-IID) data distribution, leading to performance degradation and optimization objective bias in FL training. With the development of 5G/6G networks and the integration of cloud computing… More >

  • Open Access

    ARTICLE

    MV-Honeypot: Security Threat Analysis by Deploying Avatar as a Honeypot in COTS Metaverse Platforms

    Arpita Dinesh Sarang, Mohsen Ali Alawami, Ki-Woong Park
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 655-669, 2024, DOI:10.32604/cmes.2024.053434
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract Nowadays, the use of Avatars that are unique digital depictions has increased by users to access Metaverse—a virtual reality environment—through multiple devices and for various purposes. Therefore, the Avatar and Metaverse are being developed with a new theory, application, and design, necessitating the association of more personal data and devices of targeted users every day. This Avatar and Metaverse technology explosion raises privacy and security concerns, leading to cyber attacks. MV-Honeypot, or Metaverse-Honeypot, as a commercial off-the-shelf solution that can counter these cyber attack-causing vulnerabilities, should be developed. To fill this gap, we study user’s More >

    Graphic Abstract

    MV-Honeypot: Security Threat Analysis by Deploying Avatar as a Honeypot in COTS Metaverse Platforms

  • Open Access

    REVIEW

    An Investigation on Open-RAN Specifications: Use Cases, Security Threats, Requirements, Discussions

    Heejae Park, Tri-Hai Nguyen, Laihyuk Park
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 13-41, 2024, DOI:10.32604/cmes.2024.052394
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract The emergence of various technologies such as terahertz communications, Reconfigurable Intelligent Surfaces (RIS), and AI-powered communication services will burden network operators with rising infrastructure costs. Recently, the Open Radio Access Network (O-RAN) has been introduced as a solution for growing financial and operational burdens in Beyond 5G (B5G) and 6G networks. O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs. By disaggregating conventional Base Band Units (BBUs) into O-RAN Distributed Units (O-DU) and O-RAN Centralized Units (O-CU), O-RAN offers greater flexibility for upgrades and network automation. However, this openness introduces new security More >

  • Open Access

    ARTICLE

    Malware Detection Using Dual Siamese Network Model

    ByeongYeol An, JeaHyuk Yang, Seoyeon Kim, Taeguen Kim
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 563-584, 2024, DOI:10.32604/cmes.2024.052403
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract This paper proposes a new approach to counter cyberattacks using the increasingly diverse malware in cyber security. Traditional signature detection methods that utilize static and dynamic features face limitations due to the continuous evolution and diversity of new malware. Recently, machine learning-based malware detection techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have gained attention. While these methods demonstrate high performance by leveraging static and dynamic features, they are limited in detecting new malware or variants because they learn based on the characteristics of existing malware. To overcome these limitations, malware… More >

  • Open Access

    ARTICLE

    A Novel Framework to Construct S-Box Quantum Circuits Using System Modeling: Application to 4-Bit S-Boxes

    Yongjin Jeon, Seungjun Baek, Jongsung Kim
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 545-561, 2024, DOI:10.32604/cmes.2024.052374
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract Quantum computers accelerate many algorithms based on the superposition principle of quantum mechanics. The Grover algorithm provides significant performance to malicious users attacking symmetric key systems. Since the performance of attacks using quantum computers depends on the efficiency of the quantum circuit of the encryption algorithms, research research on the implementation of quantum circuits is essential. This paper presents a new framework to construct quantum circuits of substitution boxes (S-boxes) using system modeling. We model the quantum circuits of S-boxes using two layers: Toffoli and linear layers. We generate vector spaces based on the values… More >

  • Open Access

    ARTICLE

    Cross-Domain Bilateral Access Control on Blockchain-Cloud Based Data Trading System

    Youngho Park, Su Jin Shin, Sang Uk Shin
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 671-688, 2024, DOI:10.32604/cmes.2024.052378
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract Data trading enables data owners and data requesters to sell and purchase data. With the emergence of blockchain technology, research on blockchain-based data trading systems is receiving a lot of attention. Particularly, to reduce the on-chain storage cost, a novel paradigm of blockchain and cloud fusion has been widely considered as a promising data trading platform. Moreover, the fact that data can be used for commercial purposes will encourage users and organizations from various fields to participate in the data marketplace. In the data marketplace, it is a challenge how to trade the data securely… More >

  • Open Access

    ARTICLE

    Anomaly Detection in Imbalanced Encrypted Traffic with Few Packet Metadata-Based Feature Extraction

    Min-Gyu Kim, Hwankuk Kim
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 585-607, 2024, DOI:10.32604/cmes.2024.051221
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract In the IoT (Internet of Things) domain, the increased use of encryption protocols such as SSL/TLS, VPN (Virtual Private Network), and Tor has led to a rise in attacks leveraging encrypted traffic. While research on anomaly detection using AI (Artificial Intelligence) is actively progressing, the encrypted nature of the data poses challenges for labeling, resulting in data imbalance and biased feature extraction toward specific nodes. This study proposes a reconstruction error-based anomaly detection method using an autoencoder (AE) that utilizes packet metadata excluding specific node information. The proposed method omits biased packet metadata such as… More >

  • Open Access

    ARTICLE

    A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

    Borja Bordel Sánchez, Ramón Alcarria, Tomás Robles
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 631-654, 2024, DOI:10.32604/cmes.2024.050349
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into… More >

  • Open Access

    ARTICLE

    Time Parameter Based Low-Energy Data Encryption Method for Mobile Applications

    Li-Woei Chen, Kun-Lin Tsai, Fang-Yie Leu, Wen-Cheng Jiang, Shih-Ting Tseng
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2779-2794, 2024, DOI:10.32604/cmes.2024.052124
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract Various mobile devices and applications are now used in daily life. These devices require high-speed data processing, low energy consumption, low communication latency, and secure data transmission, especially in 5G and 6G mobile networks. High-security cryptography guarantees that essential data can be transmitted securely; however, it increases energy consumption and reduces data processing speed. Therefore, this study proposes a low-energy data encryption (LEDE) algorithm based on the Advanced Encryption Standard (AES) for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things (IoT) devices. In the proposed LEDE algorithm, the system time More >

  • 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, Vol.140, No.2, pp. 1669-1687, 2024, 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… 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,… More >

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