Advancements in Distributed Intelligent Security Through AI-Driven Solutions

Dear Colleagues,

Rapid advancements in artificial intelligence (AI) are revolutionizing the cybersecurity landscape, particularly in distributed computing environments. With the growing dependence on decentralized systems, such as edge computing, the Internet of Things (IoT), and federated learning, traditional centralized security frameworks are increasingly being outpaced by the complexity and scale of modern networks. These emerging environments require advanced, intelligent security solutions capable of adapting to ever-evolving threats and vulnerabilities.

This Special Issue seeks to explore the intersection of AI and distributed intelligent security, presenting groundbreaking research that leverages AI to address the unique challenges posed by distributed systems. AI-driven security solutions hold the promise of transforming cybersecurity by enabling real-time detection, prevention, and mitigation of attacks in distributed environments. Through adaptive, scalable, and autonomous capabilities, AI has the potential to create more resilient security architectures that proactively protect decentralized networks and data systems.

We invite researchers, academics, and practitioners to contribute their latest insights and advancements in this rapidly evolving field. Submissions are encouraged to focus on topics that explore AI’s role in enhancing security across distributed systems, whether through novel algorithms, theoretical advancements, or practical applications. Interdisciplinary research and case studies highlighting real-world implementations are also welcomed.

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

1. AI-enhanced Intrusion Detection and Prevention Systems: New methodologies that leverage machine learning, deep learning, or other AI techniques to detect and prevent unauthorized access or anomalies in distributed systems.
2. Federated Learning Security and Privacy: Approaches for ensuring data privacy and security within federated learning frameworks, including secure model aggregation, adversarial attacks, and defense mechanisms.
3. Blockchain-based Security Protocols: Applications of blockchain technology to secure distributed environments, with a focus on decentralized trust mechanisms, consensus protocols, and secure data sharing.
4. Machine Learning for Anomaly Detection: Leveraging machine learning models to identify outliers or unusual behavior across decentralized networks, ensuring early threat detection.
5. AI-driven Encryption Techniques: Innovations in encryption driven by AI that enhance data security and privacy across distributed systems, focusing on lightweight and scalable encryption models.
6. Adaptive Cybersecurity Architectures: AI-enabled frameworks that automatically adapt to evolving threats, providing dynamic defense mechanisms for distributed systems.
7. AI for Real-time Security Analytics: Leveraging AI to process vast amounts of distributed data in real time, providing predictive and actionable insights to prevent cyberattacks.
8. AI-powered Privacy-preserving Techniques: Research on AI-driven methods for preserving user privacy across distributed systems, including differential privacy, homomorphic encryption, and secure multi-party computation.
9. Application of Generative Models in Security: The use of generative models such as GANs for cybersecurity applications, including generating synthetic data for training and simulating attack scenarios.
10. Deep Learning and Natural Language Processing for Security: Applying advanced deep learning models and NLP techniques to enhance the detection and mitigation of cyber threats, particularly in analyzing textual data like phishing emails or malicious code.

Dr. Kongyang Chen
Dr. Jianping Cai
Dr. Hao Chen
Guest Editors

Manuscript Submission Information

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• distributed systems
• artificial intelligence security
• privacy-preserving techniques
• cybersecurity