Future Trends of Cybersecurity in Intelligent Systems

Dear Colleagues,

This is a call to action for Value Alignment in Cybersecurity Intelligent Systems. The academic, governmental, and industrial Cybersecurity community must prioritize research and development into a new class of intelligent systems—ones that understand that their knowledge of human objectives is incomplete and that act to the benefit of humans under this fundamental uncertainty. As Artificial Intelligence (AI) progresses from controlled laboratory environments to real-world deployment, the standard model of AI—wherein an agent optimizes a fixed, human-specified objective—is becoming increasingly untenable. This is especially true in high-stakes domains such as Cybersecurity, where Intelligent Systems are designed to enhance defenses, automate tasks, and provide proactive threat intelligence: Security Information and Event Management (SIEM), Security Orchestration, Automation, and Response (SOAR), User and Entity Behavior Analytics (UEBA), Endpoint Detection and Response (EDR), Network Detection and Response (NDR), Extended Detection and Response (XDR), Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), Fraud Detection Systems, Phishing Detection, Malware Detection, Ransomware Detection, Zero-Day Threat Detection, Vulnerability Management, Threat Hunting, Incident Response, Security Analytics Platform, Cyber Defense AI, Autonomous Security Operations, and Digital Forensics. This Special Issue, grounded in the principles articulated by Russell and Norvig (2021), posits that the value alignment problem—the challenge of ensuring an AI's objective function corresponds to the true preferences of its human stakeholders—is a critical, urgent frontier for Cybersecurity research. A misaligned autonomous cyber-agent, unlike a system in a simulator, cannot simply be reset. The negative consequences of its actions are real, and as its intelligence increases, so too does the potential magnitude of these consequences. This Special Issue serves as a call to action for the research community to pivot towards developing provably beneficial systems that can reason and act under objective uncertainty. As Russell and Norvig (2021) argue, "When a machine knows that it does not know the complete objective, it has an incentive to act cautiously, to ask permission, to learn more about our preferences through observation, and to defer to human control". This principle suggests a clear research agenda and a call to action for the Cybersecurity community, grounded in established AI concepts: Adopt Models of Preference Uncertainty, Leverage Inverse Reinforcement Learning (IRL), and Develop and Deploy Assistance Games.

Dr. Dustin Mink
Dr. Ryan Benton
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.

• intelligent systems
• cybersecurity
• artificial intelligence (AI)
• machine learning (ML)
• deep learning (DL)
• large language models (LLM)
• retrieval-augmented generation (RAG)
• neural networks
• supervised learning
• labeled datasets
• unsupervised learning
• datasets
• reinforcement learning
• natural language processing (NLP)
• computer vision (CV)
• big data analytics
• data mining
• predictive analytics
• behavioral analytics
• anomaly detection
• pattern recognition
• automation
• orchestration
• threat intelligence (TI)
• cyber threat intelligence (CTI)
• adversarial AI
• proactive security
• adaptive security
• self-learning systems
• cognitive security