Advancements in AI-Driven Cybersecurity and Securing AI Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 721
Special Issue Editor
Interests: classification; network security; it security; computer networking; cloud computing; security; cyber security; information security; network communication; networking; homomorphic encryption
Special Issue Information
Dear colleagues,
Artificial Intelligence (AI) and Machine Learning (ML) have transformed the field of cybersecurity, providing the ability to analyze vast datasets quickly, detect threats, and enhance the overall security posture. However, these advancements also bring new challenges, as AI systems themselves become targets for exploitation. Cyber adversaries can manipulate AI techniques to bypass defenses, exposing vulnerabilities in AI-powered applications, including those in critical sectors like healthcare, finance, and infrastructure. Addressing both the potential and risks associated with AI in cybersecurity is essential to secure and leverage these technologies effectively.
The purpose of this Special Issue is to gather original research articles that reflect innovative methods in the following two primary tracks within the intersection of AI and cybersecurity:
- Track 1: AI for Cybersecurity focuses on integrating AI to enhance cybersecurity defenses. Relevant areas include AI-driven threat intelligence, intrusion detection systems (IDS), malware analysis, anomaly detection, and cybersecurity automation. This track encourages research that introduces innovative AI applications to meet the complexities of modern cyber threats, optimizing defensive strategies, and enhancing decision-making processes.
- Track 2: Security of AI explores the critical challenges of protecting AI systems, including Generative AI, from evolving threats that could compromise their reliability, trustworthiness, and operational stability. This track covers research on adversarial resilience, model robustness, secure training practices, and privacy-preserving AI techniques. It also invites studies on securing Generative AI systems, including large language models (LLMs), which are vulnerable to prompt injection attacks, backdoor exploits, and adversarial manipulations.
This Special Issue seeks contributions that capture theoretical, methodological, and practical advancements in both AI-enabled cybersecurity and securing AI technologies. While key topics are suggested for each track, the scope remains open to a broad range of scholarly perspectives that advance this interdisciplinary dialogue.
Dr. Mohammad GhasemiGol
Guest Editor
Manuscript Submission Information
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Keywords
- AI-driven threat intelligence
- Intrusion detection systems (IDS)
- Malware analysis
- Anomaly detection
- Cybersecurity automation
- AI-enhanced security defenses
- Cyber threat analysis
- Adversarial resilience
- Model robustness
- Secure AI training
- Privacy-preserving AI
- Data integrity in AI
- Generative AI security
- Large language models (LLMs)
- Prompt injection attacks
- Backdoor exploits in AI
- AI system vulnerabilities
- Trustworthiness of AI systems
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