Cybersecurity in AI-Driven Systems: Challenges and Innovations
A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".
Deadline for manuscript submissions: 15 October 2026 | Viewed by 161
Special Issue Editors
Interests: cryptography; blockchain applications; AI cybersecurity; internet of things (IoT) security; artificial intelligence/machine learning/deep learning on resource-constrained devices; cyber threat Intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: Artificial Intelligence, Machine Learning, Deep Learning, Federated Learning, Internet of Things (IoT), Human Behavior Analysis, Cybersecurity, Serious Games, ICT Integration in Science Education and Learning
Special Issues, Collections and Topics in MDPI journals
Interests: AI-driven cybersecurity; offensive cybersecurity; advanced pentesting; AI-assisted and generative AI-based pentesting; red teaming; cyber kill chain; cybersecurity maturity assessment; machine learning for threat detection; SOC automation and analytics; cyber threat intelligence; security governance; risk and compliance (GRC)
Interests: continuous authentication; encryption and decryption; security monitoring; anomaly detection; behavioral scores; false acceptance rate; internet of things; formal verification
Special Issue Information
Dear Colleagues,
The rapid integration of Artificial Intelligence (AI) into modern systems has revolutionized various technological domains, offering unprecedented capabilities in automation, prediction, and decision-making. However, this AI-driven evolution introduces significant cybersecurity challenges, including adversarial attacks that manipulate AI models, AI-powered threats such as sophisticated malware, deepfakes, and prompt injections, privacy breaches in data-intensive AI training, and vulnerabilities in AI-deployed environments like IoT, edge devices, autonomous systems, and smart cities. At the same time, AI presents innovative opportunities to strengthen cybersecurity through advanced anomaly detection, automated threat response, predictive intelligence, and lightweight defenses on resource-constrained platforms.
This Special Issue aims to address the dual nature of AI in cybersecurity, focusing on the challenges posed by and to AI-driven systems, as well as emerging innovations for mitigation and defense. We welcome original research articles, review papers, and case studies that provide novel insights into these interdisciplinary topics. Topics of interest include, but are not limited to, the following:
- Cybersecurity challenges and innovations in LLMs and generative AI;
- Adversarial machine learning and robust AI models;
- AI-enhanced threat detection and intrusion detection systems;
- Privacy-preserving AI techniques (e.g., federated learning and differential privacy);
- Cybersecurity challenges and innovations in AI-driven big data systems;
- Cybersecurity in TinyML: Challenges and lightweight defenses for edge AI devices;
- Cybersecurity for AI in IoT and edge computing;
- Deepfake detection and mitigation;
- AI-driven cyber attacks and defense strategies;
- Explainable AI for security applications;
- Vulnerability assessment in AI systems;
- Ethical and regulatory aspects of AI in cybersecurity;
- Secure machine learning pipelines and model hardening;
- AI for cyber threat intelligence and predictive analytics;
- Blockchain and AI integration for enhanced security;
- Cybersecurity in AI-driven smart cities and urban infrastructure;
- Cybersecurity challenges and innovations in intelligent robotics and autonomous agents;
- Cybersecurity in autonomous and embedded AI systems;
- AI-driven security in Software-Defined Networking: Challenges and innovations;
- Zero-trust architectures with AI components;
- Quantum threats to AI-driven systems and post-quantum cryptography.
Prof. Dr. Khalid El Makkaoui
Prof. Dr. Ibrahim Ouahbi
Dr. Mounia Zaydi
Prof. Dr. Belhassen Zouari
Guest Editors
Manuscript Submission Information
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Keywords
- adversarial machine learning
- generative AI security
- deepfake detection
- privacy-preserving AI
- AI-enhanced threat detection
- cybersecurity in IoT/edge
- TinyML security
- explainable AI security
- AI-driven cyber attacks
- robust AI models
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