AI-Enhanced Security: Advancing Threat Detection and Defense

Dear Colleagues,

The rapid advancement of artificial intelligence (AI) has opened new frontiers in the field of security, offering powerful tools to detect, prevent, and respond to evolving cyber threats. This Special Issue, AI-Enhanced Security: Advancing Threat Detection and Defense, will explore the intersection of AI, cybersecurity, and broader computer science applications to foster innovative research and practical solutions in digital defense.

We invite original research papers, surveys, and case studies that leverage AI to address pressing challenges in security. While traditional topics such as AI-driven network security, intrusion detection, and anomaly detection remain central, we also welcome contributions that go beyond network-focused applications. This includes, but is not limited to, AI methods for malware analysis, fraud detection, deepfake and fake image recognition, biometric security, data privacy, digital forensics, adversarial machine learning, and secure AI model deployment.

This Special Issue will also showcase cutting-edge research that not only applies AI techniques to improve cybersecurity but also contributes to the advancement of computer science and artificial intelligence as disciplines. We particularly encourage interdisciplinary work that bridges theory and practice, as well as innovative solutions that enhance resilience, trustworthiness, and interpretability in AI-powered security systems.

Topics of interest include, but are not limited to, the following:

• AI-driven intrusion detection and prevention systems;
• Anomaly detection in network and system behavior;
• Machine learning for malware and ransomware analysis;
• Deep learning for phishing and fraud detection;
• Adversarial machine learning and robust AI models for security;
• AI-powered digital forensics and threat intelligence;
• Detection of fake media (e.g., deepfakes, synthetic images, and videos);
• AI-enhanced biometric authentication and access control;
• Natural language processing for spam, phishing, and social engineering detection;
• AI for privacy-preserving data analysis and secure multi-party computation;
• Reinforcement learning for adaptive cybersecurity strategies;
• AI in vulnerability assessment and automated penetration testing;
• Secure deployment and monitoring of AI models in real-world systems;
• Ethical and legal considerations in AI-powered security systems

Dr. Ning Yu
Prof. Dr. Wei Zhong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence in security
• cybersecurity
• threat detection
• anomaly detection
• adversarial machine learning
• digital forensics
• deepfake detection
• AI-driven intrusion detection systems
• privacy-preserving AI
• secure AI deployment