Adversarial, Offensive, and Defensive AI for Future Internet Security
A Special Issue of Future Internet (ISSN 1999-5903) belonging to the section "Big Data and Augmented Intelligence".
Deadline for manuscript submissions: 30 June 2027 | Viewed by 126
Editors
Interests: artificial intelligence (AI); deep learning; internet of things; cybersecurity intelligence; generative AI; agentic AI
Interests: artificial intelligence; soft computing; support vector machine
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid evolution of the Future Internet, driven by the widespread adoption of the Internet of Things (IoT), Industrial IoT (IIoT), cyber–physical systems (CPSs), edge–cloud computing, software-defined networking (SDN), network function virtualization (NFV), and emerging 5G/6G infrastructures, has significantly expanded the cyber threat surface. Artificial intelligence has become a double-edged sword in this landscape: deep learning, reinforcement learning, generative AI, and large language models now power defensive capabilities such as anomaly detection, threat intelligence, and automated incident response, while the same techniques are increasingly weaponized for reconnaissance automation, phishing generation, evasive malware, and deepfake-driven social engineering. AI models themselves have also become targets of attack, vulnerable to adversarial examples, prompt injection, data poisoning, model inversion, and jailbreaks.
This Special Issue invites original research, comprehensive reviews, and application-oriented studies at the intersection of offensive, defensive, and adversarial AI across Future Internet infrastructures. We welcome contributions addressing theoretical advances, practical implementations, benchmarks, datasets, and real-world deployments that strengthen the security, resilience, and trustworthiness of AI-driven systems at the Internet scale.
Topics of interest include, but are not limited to, the following:
- Adversarial machine learning and robust AI systems.
- Agentic AI for autonomous cyber defense and offensive security.
- Large language models (LLMs) and foundation models for cybersecurity.
- Foundation models for vulnerability discovery and software security.
- GenAI for the generation of malware and evasive cyber threats.
- AI-powered cyberattack automation and offensive security.
- AI-aided penetration testing and intelligent red teaming.
- Generative AI for cyber deception and active defense.
- Autonomous cyber resilience and self-healing Internet systems.
- Deep learning for cyber threat intelligence and attack attribution.
- AI agents for Security Operations Centers (AI-SOCs).
- Multi-agent systems for collaborative cyber operations.
- Collaborative machine learning for distributed cyber defense.
- AI-enhanced security for digital twins and smart infrastructures.
- Convergence of AI and the Zero Trust Architecture (ZTA) and adaptive access control.
- AI-driven security for advanced edge intelligence and the cloud–edge continuum.
- Convergence of AI and software-defined networking (SDN), network slicing, and 5G/6G security.
- Benchmark datasets and evaluation frameworks for AI security.
- Secure deployment and governance of AI models in Internet-scale environments.
- Ethical, legal, governance, and regulatory issues in AI-driven cybersecurity.
Dr. Hossam Hawash
Dr. Adam Slowik
Guest Editors
Manuscript Submission Information
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Keywords
- adversarial machine learning
- offensive AI
- defensive security frameworks
- automated vulnerability discovery
- edge and IoT security
- autonomous threat hunting
- generative AI and deepfakes
- explainable AI (XAI) for cyber defense
- zero-trust orchestration
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