Special Issues
Table of Content

Unveiling the Role of AIGC, Large Models, and Human - Centric Insights in Digital Defense

Submission Deadline: 31 May 2024 (closed) View: 61

Guest Editors

Dr. Affan Yasin, Northwestern Polytechnical University (NPU), China
Prof. Zhili Zhou, Guangzhou University, China
Prof. Weizhi Meng, Technical University of Denmark (DUT), Denmark
Dr. Javed Ali Khan, University of Hertfordshire, UK
Dr. Chen Qian, Tsinghua y University, China

Summary

As digital technologies and internet-connected systems become ubiquitous across public, private, and commercial spheres, new cybersecurity threats seem to emerge daily. Recent high-profile hacks and data breaches demonstrate the growing sophistication of malicious actors and the inability of conventional cyber defenses to keep pace. Clearly we need more innovative, proactive approaches to enhance digital security in the modern hyperconnected world. This requires moving beyond siloed disciplinary perspectives and leveraging insights across computing, social sciences, business, law, and humanities.

 

This special issue aims to catalyze fresh interdisciplinary collaborations and perspectives to strengthen the reliability, resilience, and trustworthiness of our digital ecosystems against misuse and attack. We seek to break down traditional academic barriers and synthesise diverse vantage points on bolstering cyber defenses while maintaining ethics, privacy, and human values. The latest advances in adversarial machine learning, decentralized blockchain architectures, risk modeling, behavioral psychology, and regulation all have important roles to play. By fostering creative cross-pollination between these fields, we hope to spur promising new directions for research and practice.

 

Possible topics for study submission and are not limited to those listed below.

 

1. Technical Approaches

(1) Artificial intelligence security applications

(2) Cross-model generative models and foundation models

(3) Copyright protection of Artificial Intelligence Generated Content (AIGC)

(4) Adversarial machine learning

(5) Production and Detection of AIGC

(6) Adversarial attacks in AIGC

(7) Privacy protection in AIGC

(8) Identity authentication in AIGC

(9) Digital forensics for AIGC

 

2. Deception technology

 

3. Social Engineering / Human Factor

(1) Behavioral Methods

(2) Human factors in cybersecurity (Phishing Attacks, Smishing, Vishing, Phishing  etc)

(3) Usable security

(4) Social engineering defenses

 

4. Other Topics

(1) Game design and theory

(2) Privacy enhancing technologies Or theory

(3) Cybercrime deterrence and Policy


Keywords

Artificial Intelligence Generated Content (AIGC), Social Engineering, Phishing Attack, Digital Defence

Published Papers


  • Open Access

    ARTICLE

    Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

    Marya Iqbal, Yaser Hafeez, Nabil Almashfi, Amjad Alsirhani, Faeiz Alserhani, Sadia Ali, Mamoona Humayun, Muhammad Jamal
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5031-5049, 2024, DOI:10.32604/cmc.2024.051371
    (This article belongs to the Special Issue: Unveiling the Role of AIGC, Large Models, and Human - Centric Insights in Digital Defense)
    Abstract Embracing software product lines (SPLs) is pivotal in the dynamic landscape of contemporary software development. However, the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability, underscoring the critical importance of robust cybersecurity measures. This paper advocates for leveraging machine learning (ML) to address variability management issues and fortify the security of SPL. In the context of the broader special issue theme on innovative cybersecurity approaches, our proposed ML-based framework offers an interdisciplinary perspective, blending insights from computing, social sciences, and business. Specifically, it employs ML for demand analysis, More >

  • Open Access

    ARTICLE

    Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

    Aiman, Muhammad Arshad, Bilal Khan, Sadique Ahmad, Muhammad Asim
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3333-3353, 2024, DOI:10.32604/cmc.2024.049254
    (This article belongs to the Special Issue: Unveiling the Role of AIGC, Large Models, and Human - Centric Insights in Digital Defense)
    Abstract Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personal information, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic, semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, and marketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French, etc. However, the research on Roman Urdu is not up to the mark. Hence, this study focuses on detecting the author’s age and gender based on Roman Urdu text messages.… More >

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