AI-Driven Approaches for Cybersecurity and Privacy Protection: Methods, Systems, and Applications

Dear Colleagues,

The rapid expansion of connected systems, cloud services, IoT/edge devices, and data-driven applications has made cybersecurity and privacy protection more complex than ever. Attackers increasingly exploit automation, scale, and AI-enabled attack strategies, while defenders must operate under strict constraints on latency, cost, interpretability, and regulatory compliance. Meanwhile, AI has quickly evolved into a powerful toolkit for analyzing massive security data streams, identifying subtle attack patterns, and enabling faster and more adaptive responses. At the same time, the adoption of AI introduces new risks, such as adversarial manipulation, model leakage, and unintended disclosure, making it essential to develop solutions that are not only effective but also robust, trustworthy, and privacy-aware.

In this Special Issue, titled “AI-Driven Approaches for Cybersecurity and Privacy Protection: Methods, Systems, and Applications”, we aim to explore innovative AI-driven techniques that strengthen cyber defenses while safeguarding sensitive information. From learning-based intrusion detection and malware analysis to privacy-preserving analytics and secure deployment, this Special Issue will highlight the latest research advances, methodological developments, and practical implementations that push the boundaries of security and privacy protection across modern digital infrastructures. We seek contributions that span algorithms, systems, and applications, bridging theoretical insights with reproducible evaluations and real-world impact.

We invite contributions that investigate cutting-edge AI approaches for cybersecurity and privacy, including both attack-aware and defense-oriented perspectives. Whether a submission focuses on new models for threat detection, scalable security analytics for operational settings, privacy-enhancing technologies for data sharing and collaborative learning, or AI-enabled security solutions for specific domains (e.g., healthcare, finance, industrial IoT, smart cities, transportation, and critical infrastructure), we welcome studies that demonstrate novelty, rigor, and practical relevance.

Suitable topics include, but are not limited to, the following:

• AI/ML/DL methods for intrusion detection, anomaly detection, and network traffic analysis.
• Malware, ransomware, phishing, spam, and fraud detection, classification, and attribution.
• Threat intelligence mining, security analytics, alert correlation, and SOC automation (SOAR).
• Vulnerability discovery, exploit prediction, patch prioritization, and secure software/code analysis.
• Adversarial machine learning for security: evasion/poisoning/backdoors and corresponding defenses.
• Privacy-preserving AI and analytics, including federated learning, differential privacy, and secure aggregation.
• Secure computation for privacy protection (e.g., MPC, homomorphic encryption, trusted execution environments).
• Data anonymization, de-identification, synthetic data generation, and privacy risk assessment.
• Trustworthy and explainable security AI: interpretability, auditing, uncertainty, robustness, and reliability.
• AI security and privacy for IoT/edge, cloud-native systems, cyber–physical systems, and 5G/6G networks.
• Secure MLOps and AI governance for security/privacy, including monitoring, compliance, and reproducible pipelines.
• Datasets, benchmarks, measurement studies, and real-world deployment case studies and lessons learned.

We look forward to your contributions, which will help to broaden understanding and accelerate progress in AI-driven cybersecurity and privacy protection. Your work can pave the way for new, practical, and scalable solutions that enhance digital resilience, reduce privacy risks, and support trustworthy deployment across diverse applications and industries.

Please do not hesitate to contact us if you have any questions or require additional information regarding your submission.

Dr. Jiao Yin
Prof. Dr. Zhanshan Zhao
Dr. Guihong Chen
Guest Editors

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