Security Challenges and Opportunities of Artificial Intelligence/Big Data Scenarios

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2107

Special Issue Editors


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Guest Editor
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: artificial intelligence and big data security; satellite internet security; optical communication systems and optical network security
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Guest Editor
Department of Mechanical and Electrical Engineering, Hunan University, Changsha 410082, China
Interests: big data privacy protection; intelligent buildings; information-based building management systems; energy consumption prediction
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: big data privacy protection; multi-modality learning; interactive AI for vision

Special Issue Information

Dear Colleagues,

The convergence of Artificial Intelligence (AI) and Big Data has transformative implications across various sectors, offering unparalleled prospects for innovation and operational efficiency. AI systems, bolstered by the depth and breadth of Big Data, are poised to revolutionize decision making, predictive analytics, and personalized services. The fusion of AI and Big Data offers immense innovation, but it also poses acute security challenges. As AI systems leverage vast data for enhanced decision making, they expose vulnerabilities and sensitive data risks. The security environment is fraught with challenges, from the vulnerabilities embedded within AI algorithms to the management of vast troves of sensitive data. The risks posed by data breaches, algorithmic biases, and the misuse of AI for malicious intent are significant and demand immediate attention.

This Special Issue is particularly interested in technical, experimental, and methodological contributions that explore the interface between security and the advancement of AI and Big Data. We encourage the submission of papers that offer innovative approaches, strategies, and applications aimed at mitigating security risks and enhancing the trustworthiness of AI and Big Data systems. Special consideration will be given to research that advances our understanding of how to secure these technologies for the betterment of industrial processes, user safety, and overall system integrity.

Suggest themes.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Intrinsic AI security;
  • Big Data security;
  • Derived AI security;
  • Data breaches;
  • AI-enabled security;
  • Malicious use of AI;
  • Ethical AI;
  • Regulatory frameworks;
  • Predictive analytics;
  • Privacy protection;
  • Trustworthy biometrics;
  • Security in satellite network;
  • Cybersecurity;
  • Algorithmic bias;
  • AI data security;
  • AI model security

Dr. Xiaodan Yan
Prof. Dr. Ke Yan
Dr. Muyi Sun
Guest Editors

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Keywords

  • artificial intelligence
  • security and privacy protection
  • privacy protection
  • vulnerability
  • cybersecurity
  • secure communication

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Published Papers (2 papers)

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Research

19 pages, 1821 KiB  
Article
Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
by Fangzhou Meng, Xiaodan Yan, Yuanjian Zhang, Jian Yang, Ang Cao, Ruiqi Liu and Yongli Zhao
Electronics 2025, 14(12), 2376; https://doi.org/10.3390/electronics14122376 - 10 Jun 2025
Viewed by 403
Abstract
In this paper, we focus on defending against distributed denial-of-service (DDoS) attacks in a low-earth-orbit (LEO) satellite network (LSN). To enhance the security of LSN, we propose the K-Bottleneck Minimize routing method. The algorithm ensures path diversity while avoiding vulnerable bottleneck paths, which [...] Read more.
In this paper, we focus on defending against distributed denial-of-service (DDoS) attacks in a low-earth-orbit (LEO) satellite network (LSN). To enhance the security of LSN, we propose the K-Bottleneck Minimize routing method. The algorithm ensures path diversity while avoiding vulnerable bottleneck paths, which significantly increases the cost for attackers. Additionally, the attacker’s detectability is reduced. The results show that the algorithm avoids the bottleneck paths that are vulnerable to attacks, improves the attacker’s cost by about 13.1% and 16.6% on average and median, and improves the detectability of attackers by 48.5% and 45.4% on average and median. The algorithm generates multiple non-overlapping inter-satellite paths, preventing the exploitation of bottleneck paths and ensuring better robustness and attack resistance. Full article
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20 pages, 1186 KiB  
Article
A Practical Human-Centric Risk Management (HRM) Methodology
by Kitty Kioskli, Eleni Seralidou and Nineta Polemi
Electronics 2025, 14(3), 486; https://doi.org/10.3390/electronics14030486 - 25 Jan 2025
Cited by 1 | Viewed by 1150
Abstract
Various standards (e.g., ISO 27000x, ISO 31000:2018) and methodologies (e.g., NIST SP 800-53, NIST SP 800-37, NIST SP 800-161, ETSI TS 102 165-1, NISTIR 8286) are available for risk assessment. However, these standards often overlook the human element. Studies have shown that adversary [...] Read more.
Various standards (e.g., ISO 27000x, ISO 31000:2018) and methodologies (e.g., NIST SP 800-53, NIST SP 800-37, NIST SP 800-161, ETSI TS 102 165-1, NISTIR 8286) are available for risk assessment. However, these standards often overlook the human element. Studies have shown that adversary profiles (AP), which detail the maturity of attackers, significantly affect vulnerability assessments and risk calculations. Similarly, the maturity of the users interacting with the Information and Communication Technologies (ICT) system in adopting security practices impacts risk calculations. In this paper, we identify and estimate the maturity of user profiles (UP) and propose an enhanced risk assessment methodology, HRM (based on ISO 27001), that incorporates the human element into the risk evaluation. Social measures, such as awareness programs, training, and behavioral interventions, alongside technical controls, are included in the Human-Centric Risk Management (HRM) risk treatment phase. These measures enhance user security hygiene and resilience, reducing risks and ensuring comprehensive security strategies in SMEs. Full article
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