Advances in Mobile Internet Security

Submission Deadline: 31 March 2023 (closed)

Guest Editors

Dr. Ilsun You, Kookmin University, South Korea.
Dr. Antonio Skarmeta, University of Murcia, Spain.
Dr. Isaac Woungang, Ryerson University, Canada.


During the past two decades, mobile internet technologies have been dramatically growing while leading to a paradigm shift in our life. Despites their revolution, mobile internet technologies open doors to various security threats, which should be addressed to keep mobile Internet environments to be secure and trust. Even worse, the latest technologies (e.g. distributed mobility management, mobile internet of things, 5G and Beyond networks, and so forth) continuously have introduced new security challenges. Therefore, it is of paramount importance to study mobile internet security. The purpose of this special issue is to bring together the academic and industry working on different aspects, exchange ideas, and explore new research directions for addressing the challenges in mobility internet security.


It also aims to publish high quality papers, which are closely related to various theories and practical applications in mobility management to highlight the state-of-art research. In spite of focusing on security aspects, this symposium welcomes papers which are related to mobile internet technologies.


TOPICS (not limited to)


- Vulnerabilities and threats in mobile internet and networks

- Security issues and protocols for mobile internet and networks

- Privacy and trust for mobile internet and networks

- Security for mobility management

- Security for vertical handover in heterogeneous networks

- New advances in the AAA infrastructure and EAP

- Security for 5G and Beyond networks

- Security for NFV and SDN

- Security for IoT and wearable devices

- AI Security for mobile internet and networks

- Blockchain security for mobile internet and networks

- Security for mobile internet services and applications

- Others and emerging new topics


Mobile Information Security, 5G/6G Security, Cybersecurity, IoT/CPS security

Published Papers

  • Open Access


    Detecting Ethereum Ponzi Schemes Through Opcode Context Analysis and Oversampling-Based AdaBoost Algorithm

    Mengxiao Wang, Jing Huang
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1023-1042, 2023, DOI:10.32604/csse.2023.039569
    (This article belongs to this Special Issue: Advances in Mobile Internet Security)
    Abstract Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting (AdaBoost) algorithm.… More >

  • Open Access


    A Model Training Method for DDoS Detection Using CTGAN under 5GC Traffic

    Yea-Sul Kim, Ye-Eun Kim, Hwankuk Kim
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1125-1147, 2023, DOI:10.32604/csse.2023.039550
    (This article belongs to this Special Issue: Advances in Mobile Internet Security)
    Abstract With the commercialization of 5th-generation mobile communications (5G) networks, a large-scale internet of things (IoT) environment is being built. Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service (DDoS) attacks across vast IoT devices. Recently, research on automated intrusion detection using machine learning (ML) for 5G environments has been actively conducted. However, 5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data. If this data is used to train an ML model, it will likely suffer from generalization errors due to… More >

Share Link