Advances in Artificial Intelligence for Intelligent Systems: Methods, Trust, and Cyber Defense

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1879

Special Issue Editor


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Guest Editor
Center for Cyber Security and Forensics Education, Illinois Institute of Technology, Chicago, IL, USA
Interests: cyber security; Internet of Things; information systems security; technology management
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Special Issue Information

Dear Colleagues,

Intelligent systems are now foundational to modern life, from clinical decision support and smart grids to autonomous mobility and digital public services. As these systems scale, they must simultaneously improve capability, reliability, and resilience against evolving cyber threats. This Special Issue of Systems invites contributions that advance the methods, tools, and governance needed to design and operate intelligent systems that are secure, trustworthy, and auditable without sacrificing performance.

We seek submissions at the intersection of AI/ML and cybersecurity, emphasizing end-to-end viewpoints that integrate people, process, technology, and policy. Suitable works include novel algorithms; robust learning and inference; adversarial testing; privacy-preserving analytics; assurance cases; governance, risk, and compliance (GRC) models; and domain case studies demonstrating measurable impacts. We particularly welcome research connecting socio-technical considerations—such as organizational workflows, human factors, and regulatory constraints—to concrete system architectures and life-cycle practices aligned with systems thinking and practice.

This Special Issue seeks contributions that address themes including, but not limited to, the following:

  • Trustworthy, explainable, and verifiable AI for mission-critical decisions;
  • AI for cyber defense, spanning intrusion detection, anomaly detection, automated threat intelligence, OSINT, deception, and digital forensics;
  • Security of AI pipelines, including data curation, model provenance, and model-supply chain integrity;
  • Adversarial robustness, model hardening, and continuous validation;
  • Cyber–physical systems and ICS/OT security with attention to safety–security trade-offs;
  • Privacy-preserving machine learning (federated learning, secure aggregation, differential privacy) for sensitive sectors;
  • Governance models, auditability, and compliance frameworks for AI in regulated industries;
  • Evaluation frameworks, simulation environments, and reproducible benchmarks that accelerate adoption.

Dr. Maurice E. Dawson
Guest Editor

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Keywords

  • trustworthy AI
  • adversarial robustness
  • privacy-preserving machine learning
  • intrusion and anomaly detection
  • cyber-physical systems (CPS) security
  • federated learning
  • explainable AI (XAI)
  • AI-driven threat intelligence and OSINT
  • governance, risk, and compliance (GRC)
  • model and data supply chain security

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Published Papers (3 papers)

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Research

44 pages, 1381 KB  
Article
An AI-Enabled Cyber-Resilience Index for Industrial Control Systems: Integrating Regulatory Compliance and Geopolitical Exposure on the NATO-EU Eastern Flank
by Mircea Boșcoianu, Veaceslav Samburschii, Alexandru Silviu Goga and Marius Viorel Posa
Systems 2026, 14(6), 606; https://doi.org/10.3390/systems14060606 - 25 May 2026
Viewed by 347
Abstract
Operational Technology (OT) and Industrial Control Systems (ICSs) along the NATO-EU eastern flank face escalating hybrid threats, yet existing cyber-resilience metrics remain IT-centric, lacking OT-specific constraints and geopolitical exposure dimensions. This paper presents a Design Science Research contribution: the development and simulation-based feasibility [...] Read more.
Operational Technology (OT) and Industrial Control Systems (ICSs) along the NATO-EU eastern flank face escalating hybrid threats, yet existing cyber-resilience metrics remain IT-centric, lacking OT-specific constraints and geopolitical exposure dimensions. This paper presents a Design Science Research contribution: the development and simulation-based feasibility demonstration of two interconnected artefacts. The first is the AI-enabled Cyber-Resilience Index (ACRI)—a composite 0–100 metric operationalized through 16 indicators across four domains (detection performance, operational continuity, governance maturity, supply-chain risk), aggregated as a three-term convex combination of capability domains with a linear subtractive supply-chain exposure penalty, weighted via AHP-based illustrative sector-reference profiles. The second is the Unified Compliance Framework (UCF), a structured R → C → E → SLO mapping linking 47 atomic regulatory requirements (NIS2, DORA, CER, AI Act, CRA) to standards (IEC 62443, ISO/IEC 27001) and auditable evidence artifacts, with a Continuous Assurance Loop operationalizing continuous control monitoring. Feasibility is demonstrated through digital twin simulation under three OT-representative threat scenarios (energy SCADA APT, railway supply-chain compromise, manufacturing ransomware). Results in simulated environments show ACRI improvement from Moderate-Risk baselines (45–61) to Adequate-Resilience thresholds (65–73); the proposed federated autoencoder–LSTM detector attains a composite Dperf of 0.883 versus 0.510 for a static ±3σ threshold baseline (a 73% relative improvement at the domain level). Sensitivity analysis confirms classification robustness (±7.3% weight perturbation; coefficient of variation below 9.1% across 10,000 Monte Carlo iterations). Critical limitations are explicit: simulation-only evidence (n=12 scenario instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. The contribution is positioned as a proof-of-concept design artifact establishing methodological foundations for OT-centric resilience assessment and compliance-to-engineering traceability, not as a field-validated operational system. Full article
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28 pages, 7571 KB  
Article
Proactive Cyber Defense: A Real-Time CTI Framework with ATT&CK–D3FEND Mapping
by Rino Jo, Han-Bin Lee, Jihun Han, Woong-Kyo Jung, Jun-Yong Lee, Tae-Young Kang, Youngsoo Kim, Byung Il Kwak, Mee Lan Han and Jungmin Kang
Systems 2026, 14(5), 575; https://doi.org/10.3390/systems14050575 - 18 May 2026
Viewed by 476
Abstract
The contemporary cyber-threat landscape is becoming increasingly diverse and complex, creating a persistent gap between situational awareness and operational response. This study presents a framework designed to bridge this gap by transforming up-to-date cyber-threat intelligence (CTI) into standardized knowledge structures and actionable defense [...] Read more.
The contemporary cyber-threat landscape is becoming increasingly diverse and complex, creating a persistent gap between situational awareness and operational response. This study presents a framework designed to bridge this gap by transforming up-to-date cyber-threat intelligence (CTI) into standardized knowledge structures and actionable defense measures. First, the proposed framework integrates the threat data collected from OpenCTI and normalizes them based on the MITRE ATT&CK tactics and techniques matrix. It then leverages a large language model to automatically generate diverse threat scenarios based on the analyzed intelligence. Each scenario is organized as a tactic sequence, and individual techniques are mapped to MITRE D3FEND defensive categories based on official ATT&CK–D3FEND relationships and structured contextual interpretation. Finally, the framework produces outputs in the form of a Defense Description that includes the corresponding technique IDs, recommended defense strategies, supporting rationales, and prerequisites. An evaluation using several recent cases demonstrates that the proposed framework effectively connects current threat intelligence with practical defense strategies. In summary, the proposed framework strengthens proactive cyber defense by directly linking structured attack flows to actionable context-aware defensive techniques. In addition, this framework provides a structured pipeline that systematizes and automates steps conventionally performed manually, thereby reducing repetitive analyst effort. Full article
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39 pages, 10441 KB  
Article
IRAS-SDLC: Lifecycle Risk Aggregation for Secure AI-Augmented Software Assurance Under RMF and Zero Trust
by Samson Quaye, Maurice Dawson and Ahmed Ben Ayed
Systems 2026, 14(5), 546; https://doi.org/10.3390/systems14050546 - 11 May 2026
Viewed by 466
Abstract
Modern machine learning approaches for vulnerability detection achieve strong performance within specific datasets, yet their reliability degrades under domain shift, limiting their effectiveness for real-world secure software development lifecycle (SDLC) decision-making. In particular, probabilistic vulnerability predictions, while well-calibrated, exhibit instability across heterogeneous codebases, [...] Read more.
Modern machine learning approaches for vulnerability detection achieve strong performance within specific datasets, yet their reliability degrades under domain shift, limiting their effectiveness for real-world secure software development lifecycle (SDLC) decision-making. In particular, probabilistic vulnerability predictions, while well-calibrated, exhibit instability across heterogeneous codebases, reducing their suitability as standalone risk indicators. This paper introduces Intelligent Risk-Adaptive Secure SDLC (IRAS-SDLC), a lifecycle risk aggregation framework for Secure AI-Augmented Software Assurance under the Risk Management Framework (RMF) and Zero Trust. The proposed framework integrates model-derived vulnerability likelihood with structured security metrics, specifically exploitability and impact derived from standardized Common Vulnerability Scoring System (CVSS) data, to construct a unified and interpretable risk representation. This formulation enables consistent prioritization across SDLC phases while aligning with RMF control families and Zero Trust continuous verification principles. By combining learned semantic signals with domain-independent security factors, IRAS mitigates the instability of vulnerability likelihood under distributional shifts and provides a more robust basis for cross-domain risk assessment. The framework embeds risk evaluation early in the SDLC, enabling proactive identification of vulnerabilities during the requirements and design phases rather than post-implementation detection. Empirical evaluation demonstrates that IRAS-SDLC maintains meaningful risk estimation under domain shift and significantly improves lifecycle outcomes. In particular, early risk identification yields negative detection latency relative to conventional methods and reduces simulated remediation costs by up to an order of magnitude. IRAS-SDLC bridges the gap between machine learning-based vulnerability prediction and governance-aligned security assurance by providing a stable, interpretable, and lifecycle-aware risk assessment mechanism that is directly compatible with RMF-based compliance workflows and Zero Trust architectures. Full article
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