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Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach

Submission Deadline: 15 April 2026 (closed) View: 2173 Submit to Special Issue

Guest Editor(s)

Prof. Dr. Ilsun You

Email: ilsunu@gmail.com

Affiliation: Department of Cybersecurity, Kookmin University, Seoul, 02707, Republic of Korea

Homepage:

Research Interests: 5G/6G security, IoT/CPS security, security protocol and formal security verification

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Assoc. Prof. Gaurav Choudhary

Email: gauch@dtu.dk

Affiliation: Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, 2800, Denmark

Homepage:

Research Interests: IoT/CPS security, 5G/6G security

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Prof. Shu Yu

Email: shui.yu@uts.edu.au

Affiliation: School of Computer Science, University of Technology Sydney, Ultimo, 2007, Australia

Homepage:

Research Interests: cybersecurity, privacy and the networking, communication aspects of big data, applied mathematics for computer science

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Summary

The mobile internet has transformed modern life by enabling ubiquitous connectivity, communication, and access to services anytime and anywhere. As the world moves deeper into digital transformation, Future Mobile Internet Technologies (FMIT) — empowered by 6G, quantum computing, generative AI, and convergence applications — promise to reshape industries, economies, and societies. Yet, these unprecedented innovations also introduce new and complex security and privacy challenges, which must be addressed to ensure trust, resilience, and sustainability in our hyper-connected future.
This special issue aims to spotlight the latest innovations in security and privacy for FMIT and their convergence applications (FMIT-CA). We invite submissions of original research articles, reviews, and case studies that explore novel approaches, frameworks, algorithms, and architectures to secure next-generation mobile internet ecosystems.

This special issue plans to include extended versions of good papers presented at the 9th KIISC International Conference on Mobile Internet Security (MobiSec’25)
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Topics of Interest

Topics include, but are not limited to:
• Emerging Threats and Countermeasures in FMIT-CA
• Advances in Mobile Malware Detection and Prevention
• Privacy and Data Protection Mechanisms for FMIT-CA
• Authentication and Access Control for Next-Generation Mobile Networks
• Secure Protocols, Formal Verification, and Standardization Efforts
• Machine Learning and AI-Driven Security for FMIT-CA
• Blockchain and Distributed Ledger-Based Security Mechanisms
• Mobile Device Management (MDM) and Adaptive Mobile Security Policies
• Secure Edge and Fog Computing Architectures for FMIT
• Quantum-Resistant Cryptography and Post-Quantum Security for FMIT-CA
• Trust, Reputation, and Identity Management in FMIT Ecosystems
• Cyber Threat Intelligence and Advanced Persistent Threats in Future Mobile Internet
• Security Frameworks for Generative AI-Enabled Mobile Services
• Privacy-Preserving Data Sharing and Federated Learning for FMIT-CA

Keywords

Future Mobile Internet Security, 5G/6G and Beyond Security, Convergence Applications Security, AI/ML for Mobile Internet Security, Quantum-Resistant Security Solutions, Blockchain and Distributed Security Models

Published Papers

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Open Access

ARTICLE

LLM-Driven Cross-Flow Modeling for Network Attack Traffic Detection
Aoran Huang, Sinuo Zhang, Haoxiang Zhu, Xiaojing Fan, Huachun Zhou
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083972
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract In Future Mobile Internet and convergence application scenarios, existing network attack traffic detection methods are insufficient in characterizing cross-flow correlations and structural dependencies during the attack process, and therefore still have limited generalization ability in complex scenarios and unknown attack identification tasks. To address this issue, this paper proposes a cross-flow modeling large language model framework, which extends the traditional detection paradigm based on single-flow features to joint modeling oriented toward cross-flow context and relational structure. Specifically, this paper constructs cross-flow context through flow sorting, grouping, and cross-group sampling, and combines an inter-flow relation matrix… More >

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ARTICLE

Robust Federated Learning for Intrusion Detection in Autonomous Vehicles against Poisoning Attacks
Ulysses Lam, Jin-Hee Cho, Hyuk Lim, Terrence Moore, Frederica Free-Nelson, Hyunjae Kang, Dan Dongseong Kim
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084062
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract Autonomous vehicles are potentially more vulnerable to cyber-attacks compared to traditional human-driven ones, as they employ electronic sensors to enable self-driving. Cybersecurity for autonomous vehicles will be crucial in the near future. However, intrusion detection systems (IDSes) for vehicles are still in the early stages. Many IDS models that claim to work for vehicles are actually built with traditional Internet datasets rather than those with real vehicle data, which is impractical in reality. In this paper, IDS models are developed with Federated Learning (FL) with the Car-Hacking and CAN-MIRGU datasets, which are obtained from real More >

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Open Access

ARTICLE

A Model-Driven Approach to Secure Device Onboarding Using a Device Security Passport
Sara Matheu, Pedro Ruzafa, Ilias Kalouptsoglou, Antonio Skarmeta, Dionysios Kehagias
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083308
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract The evolution of the Future Mobile Internet, driven by large-scale connectivity and heterogeneous device ecosystems, introduces significant challenges for securely integrating devices into operational environments. Existing onboarding mechanisms primarily focus on authentication and credential provisioning, while security policy enforcement is typically deferred, creating a temporal gap during which devices may operate without appropriate constraints. This paper addresses this limitation by enabling policy enforcement during onboarding. To this end, we propose a model-driven approach that integrates the Device Security Passport (DSP) with the FIDO Device Onboard (FDO) protocol. The DSP is a lifecycle-aware model that aggregates… More >

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Open Access

ARTICLE

5G-SliceMatch: A Slice-Aware Semi-Supervised Learning Framework for Malicious Traffic Detection in 5G Networks
Jinha Kim, Hwankuk Kim
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082504
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract The advent of 5th Generation (5G) mobile networks has introduced Network Slicing as a core mechanism for supporting heterogeneous vertical services—such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC) over a shared physical infrastructure, thereby significantly expanding the attack surface at the User Plane Function (UPF). Securing this multi-slice environment requires intrusion detection systems that can simultaneously accommodate the statistical heterogeneity of per-slice traffic and the stringent Quality of Service (QoS) constraints of real-time slices, yet the practical cost of obtaining high-quality labeled traffic in operational 5G cores remains… More >

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178
Open Access

ARTICLE

Performance Analysis of an AI-Based IDS xApp for Cyberattack Anomaly Detection in O-RAN Near-RT RIC
Hyeonsoo Yu, Hwankuk Kim
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.082095
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract The introduction of the Open Radio Access Network (O-RAN) architecture enhances network flexibility but introduces novel security threats targeting open interfaces and the RAN Intelligent Controller (RIC). Particularly in the Near-RT RIC environment, an effective Intrusion Detection System (IDS) that satisfies strict near-real-time constraints of within 1 s is essential to defend against cyber attacks. This paper proposes an Artificial Intelligence (AI)-based IDS xApp designed for real-time cyber attack monitoring in the O-RAN Near-RT RIC environment, and quantitatively analyzes its anomaly detection performance and inference latency characteristics against multi-layer security threats utilizing Open RAN Centralized… More >

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ARTICLE

A Graph-Based Interpretable Framework for Effective Android Malware Detection^#
Chun-I Fan, Sheng-Feng Lu, Cheng-Han Shie, Ming-Feng Tsai, Tomohiro Morikawa, Takeshi Takahashi, Tao Ban
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077799
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract Due to its partly open-source architecture, which allows for application analysis and repackaging, along with its large market share, the Android operating system is a main target for malware. In recent years, researchers have widely adopted neural network-based methods for detecting Android malware, achieving impressive results but without interpretability. Interpretability is crucial for showing how models behave and identifying biases in their predictions, which helps in validating and improving them. Additionally, in urgent malware analysis situations, interpretability lets analysts quickly assess harmful behaviors and aids in future malware development and investigation. Therefore, interpretability is vital… More >

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ARTICLE

Machine Learning-Based Power Allocation for Covert Communication in LEO Satellite–UAV Cooperative Networks
Minjeong Kang, Jung Hoon Lee, Il-Gu Lee
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078247
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract In next-generation non-terrestrial network environments, the increasing risk of detection by unauthorized observers has motivated extensive research on covert communication approaches that minimize the probability of detection. In particular, jamming-assisted cooperative covert communication has attracted significant attention as an effective approach to simultaneously ensure communication performance and security, leading to growing interest in cooperative architectures among heterogeneous platforms. This study investigates covert communication in Low Earth Orbit (LEO) satellite–unmanned aerial vehicle (UAV) cooperative networks, where the LEO satellite serves a legitimate user, while the UAV acts as a cooperative jammer to enhance covertness. A network… More >

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ARTICLE

SCAN: Structural Clustering with Adaptive Thresholds for Intelligent and Robust Android Malware Detection under Concept Drift
Kyoungmin Roh, Seungmin Lee, Seong-je Cho, Youngsup Hwang, Dongjae Kim
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.074936
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract Many machine learning–based Android malware detection often suffers from concept drift, where models trained on historical data fail to generalize to evolving threats. This paper proposes SCAN (Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection), a hybrid intelligent framework designed to mitigate concept drift without retraining. SCAN integrates Gaussian Mixture Models (GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster. A key challenge in clustering-based malware detection is cluster-wise class imbalance, where clusters contain disproportionate distributions of benign and malicious samples. SCAN addresses this issue through adaptive thresholding, which dynamically… More >

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ARTICLE

Privacy-Aware Anomaly Detection in Encrypted Network Traffic via Adaptive Homomorphic Encryption
Yu-Ran Jeon, Seung-Ha Jee, Su-Kyoung Kim, Il-Gu Lee
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077784
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract As cyberattacks become increasingly sophisticated and intelligent, demand for machine-learning-based anomaly detection systems is growing. However, conventional systems generally assume a trusted server environment, where traffic data is collected and analyzed in plaintext. This assumption introduces inherent privacy risks, as privacy-sensitive information may be exposed if the server is compromised or misused. To address this limitation, privacy-preserving anomaly detection approaches have been actively studied, enabling anomaly detection to be performed directly on encrypted traffic without revealing privacy-sensitive data. While these approaches offer strong confidentiality guarantees, they suffer from significant drawbacks, including substantial computational overhead, high… More >

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Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach

Guest Editor(s)

Summary

Keywords

Published Papers

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34

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15

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282

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57

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567

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178

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354

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265

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950

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1419

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529

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1408

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491

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1146

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321

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