Prof. Ilsun You, Kookmin University, South Korea.
Prof. Francesco Palmieri, University of Salerno, Italy.
Prof. Karl Anderson, Luleå University of Technology, Sweden.

Summary

Over the past two decades, mobile communication and internet technologies have experienced significant growth and have brought about a paradigm shift in our lives. However, this revolution has also opened the door to various security threats that must be addressed to ensure that mobile communication and internet systems (MCIS) remain secure and trustworthy. Furthermore, emerging technologies such as 5GB/6G networks, Generative AI, Quantum Computing, autonomous driving, and others, have introduced new and complex security challenges that require attention.

The purpose of this special issue is to bring together academic and industry experts to exchange ideas and explore novel ideas and new research directions for addressing these challenges.

We welcome high-quality papers that focus on various theories and practical applications related to cybersecurity in the next generation MCIS.

Keywords

- Vulnerabilities and security threats in MCIS
- Cryptography and security protocols for MCIS
- Cryptographic software and hardware optimization for MCIS
- Privacy and trust for MCIS
- Post-Quantum cryptography for MCIS
- Security architectures and designs for MCIS
- Deployment and automation of security management for MCIS
- IoT/CPS security for MCIS
- Machine learning and AI Security for MCIS
- Blockchain security for MCIS
- Intrusion detection and prevention for MCIS

Published Papers

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Open Access

ARTICLE

IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN
Yanhua Liu, Yuting Han, Hui Chen, Baokang Zhao, Xiaofeng Wang, Ximeng Liu
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1851-1866, 2024, DOI:10.32604/cmc.2024.051194
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract As the scale of the networks continually expands, the detection of distributed denial of service (DDoS) attacks has become increasingly vital. We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network (DNN). The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible, thereby reducing data volume. Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic, enhancing the neural network’s generalization capabilities. Experimental results show that the proposed method can efficiently distinguish More >

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ARTICLE

FADSF: A Data Sharing Model for Intelligent Connected Vehicles Based on Blockchain Technology
Yan Sun, Caiyun Liu, Jun Li, Yitong Liu
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2351-2362, 2024, DOI:10.32604/cmc.2024.048903
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract With the development of technology, the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal. The data of ICV (intelligent connected vehicles) is the key to organically maximizing their efficiency. However, in the context of increasingly strict global data security supervision and compliance, numerous problems, including complex types of connected vehicle data, poor data collaboration between the IT (information technology) domain and OT (operation technology) domain, different data format standards, lack of shared trust sources, difficulty in ensuring the quality of shared data, lack of data… More >

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ARTICLE

Federated Network Intelligence Orchestration for Scalable and Automated FL-Based Anomaly Detection in B5G Networks
Pablo Fernández Saura, José M. Bernabé Murcia, Emilio García de la Calera Molina, Alejandro Molina Zarca, Jorge Bernal Bernabé, Antonio F. Skarmeta Gómez
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 163-193, 2024, DOI:10.32604/cmc.2024.051307
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract The management of network intelligence in Beyond 5G (B5G) networks encompasses the complex challenges of scalability, dynamicity, interoperability, privacy, and security. These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence (AI)-based analytics, empowering seamless integration across the entire Continuum (Edge, Fog, Core, Cloud). This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning (FL)-based anomaly detection in B5G networks. By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm, which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize… More >

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ARTICLE

Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems
Siwan Noh, Kyung-Hyune Rhee
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3805-3826, 2024, DOI:10.32604/cmc.2024.050949
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract In Decentralized Machine Learning (DML) systems, system participants contribute their resources to assist others in developing machine learning solutions. Identifying malicious contributions in DML systems is challenging, which has led to the exploration of blockchain technology. Blockchain leverages its transparency and immutability to record the provenance and reliability of training data. However, storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs. Additionally, current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data. However, less… More >

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ARTICLE

Modeling and Analysis of OFDMA-NOMA-RA Protocol Considering Imperfect SIC in Multi-User Uplink WLANs
Hailing Yang, Suoping Li, Duo Peng
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5273-5294, 2024, DOI:10.32604/cmc.2024.050869
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract To address the problems of network congestion and spectrum resources shortage in multi-user large-scale scenarios, this paper proposes a twice random access OFDMA-NOMA-RA protocol combining the advantages of orthogonal frequency division multiple access (OFDMA) and non-orthogonal multiple access (NOMA). The idea of this protocol is that OFMDA is used to divide the entire frequency field into multiple orthogonal resource units (RUs), and NOMA is used on each RU to enable more users to access the channel and improve spectrum efficiency. Based on the protocol designed in this paper, in the case of imperfect successive interference… More >

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ARTICLE

AnonymousTollPass: A Blockchain-Based Privacy-Preserving Electronic Toll Payment Model
Jane Kim, Soojin Lee, Chan Yeob Yeun, Seung-Hyun Seo
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3495-3518, 2024, DOI:10.32604/cmc.2024.050461
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract As big data, Artificial Intelligence, and Vehicle-to-Everything (V2X) communication have advanced, Intelligent Transportation Systems (ITS) are being developed to enable efficient and safe transportation systems. Electronic Toll Collection (ETC), which is one of the services included in ITS systems, is an automated system that allows vehicles to pass through toll plazas without stopping for manual payment. The ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system flow. To ensure secure and efficient toll payments, designing a distributed model for sharing toll payment information among untrusted toll service… More >

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ARTICLE

BSTFNet: An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features
Hong Huang, Xingxing Zhang, Ye Lu, Ze Li, Shaohua Zhou
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT)… More >

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ARTICLE

Enhancing Identity Protection in Metaverse-Based Psychological Counseling System
Jun Lee, Hanna Lee, Seong Chan Lee, Hyun Kwon
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 617-632, 2024, DOI:10.32604/cmc.2023.045768
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract Non-face-to-face psychological counseling systems rely on network technologies to anonymize information regarding client identity. However, these systems often face challenges concerning voice data leaks and the suboptimal communication of the client’s non-verbal expressions, such as facial cues, to the counselor. This study proposes a metaverse-based psychological counseling system designed to enhance client identity protection while ensuring efficient information delivery to counselors during non-face-to-face counseling. The proposed system incorporates a voice modulation function that instantly modifies/masks the client’s voice to safeguard their identity. Additionally, it employs real-time client facial expression recognition using an ensemble of decision… More >

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ARTICLE

Blockchain-Empowered Token-Based Access Control System with User Reputation Evaluation
Yuzheng Yang, Zhe Tu, Ying Liu, Huachun Zhou
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3163-3184, 2023, DOI:10.32604/cmc.2023.043974
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract Currently, data security and privacy protection are becoming more and more important. Access control is a method of authorization for users through predefined policies. Token-based access control (TBAC) enhances the manageability of authorization through the token. However, traditional access control policies lack the ability to dynamically adjust based on user access behavior. Incorporating user reputation evaluation into access control can provide valuable feedback to enhance system security and flexibility. As a result, this paper proposes a blockchain-empowered TBAC system and introduces a user reputation evaluation module to provide feedback on access control. The TBAC system… More >

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ARTICLE

Electromyogram Based Personal Recognition Using Attention Mechanism for IoT Security
Jin Su Kim, Sungbum Pan
CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1663-1678, 2023, DOI:10.32604/cmc.2023.043998
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract As Internet of Things (IoT) technology develops, integrating network functions into diverse equipment introduces new challenges, particularly in dealing with counterfeit issues. Over the past few decades, research efforts have focused on leveraging electromyogram (EMG) for personal recognition, aiming to address security concerns. However, obtaining consistent EMG signals from the same individual is inherently challenging, resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition. Notably, conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities. This paper proposes an innovative approach to personal recognition that combines a siamese… More >

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ARTICLE

Consortium Chain Consensus Vulnerability and Chain Generation Mechanism
Rui Qiao, Shi Dong
CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2505-2527, 2023, DOI:10.32604/cmc.2023.043476
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract Effectively identifying and preventing the threat of Byzantine nodes to the security of distributed systems is a challenge in applying consortium chains. Therefore, this paper proposes a new consortium chain generation model, deeply analyzes the vulnerability of the consortium chain consensus based on the behavior of the nodes, and points out the effects of Byzantine node proportion and node state verification on the consensus process and system security. Furthermore, the normalized verification node aggregation index that represents the consensus ability of the consortium organization and the trust evaluation function of the verification node set is… More >

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ARTICLE

Efficient Multi-Authority Attribute-Based Searchable Encryption Scheme with Blockchain Assistance for Cloud-Edge Coordination
Peng Liu, Qian He, Baokang Zhao, Biao Guo, Zhongyi Zhai
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3325-3343, 2023, DOI:10.32604/cmc.2023.041167
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract Cloud storage and edge computing are utilized to address the storage and computational challenges arising from the exponential data growth in IoT. However, data privacy is potentially risky when data is outsourced to cloud servers or edge services. While data encryption ensures data confidentiality, it can impede data sharing and retrieval. Attribute-based searchable encryption (ABSE) is proposed as an effective technique for enhancing data security and privacy. Nevertheless, ABSE has its limitations, such as single attribute authorization failure, privacy leakage during the search process, and high decryption overhead. This paper presents a novel approach called… More >

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ARTICLE

Malicious Traffic Compression and Classification Technique for Secure Internet of Things
Yu-Rim Lee, Na-Eun Park, Seo-Yi Kim, Il-Gu Lee
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3465-3482, 2023, DOI:10.32604/cmc.2023.041196
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract With the introduction of 5G technology, the application of Internet of Things (IoT) devices is expanding to various industrial fields. However, introducing a robust, lightweight, low-cost, and low-power security solution to the IoT environment is challenging. Therefore, this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment. The first method, compressed sensing and learning (CSL), compresses an event log in a bitmap format to quickly detect attacks. Then, the attack log is detected using a machine-learning classification model. The second method, precise re-learning… More >

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Innovative Security for the Next Generation Mobile Communication and Internet Systems

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