Generative AI revolution in cybersecurity: a comprehensive review of threat intelligence and operations fromArtificial Intelligence Review: This open-access comprehensive review explores how generative artificial intelligence (GenAI) is reshaping cybersecurity. It examines GenAI’s contributions to threat intelligence, automated defenses, and operational analytics, alongside the risks posed by adversarial use of generative models. The paper synthesizes recent research on AI-enabled detection, automated response mechanisms, and predictive security operations, offering a broad view of GenAI’s dual role as both a defense multiplier and a vector for sophisticated attacks.
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions (Hemanth Ravipati): This paper introduces Neuromorphic Mimicry Attacks (NMAs), a novel class of cyber threat targeting neuromorphic computing architectures inspired by biological neural systems. Ravipati outlines how attackers can exploit the probabilistic operation of neuromorphic chips to evade traditional intrusion detection systems, presenting a theoretical framework, simulated evaluations, and tailored countermeasures such as neural anomaly detection and secure learning protocols.
Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation (Tharcisse Ndayipfukamiye, Jianguo Ding, Doreen Sebastian Sarwatt, Adamu Gaston Philipo, Huansheng Ning): This systematic review examines the dual role of Generative Adversarial Networks (GANs) in cybersecurity — as both offensive tools (e.g., for generating adversarial samples) and defensive mechanisms that enhance threat detection accuracy. The authors deploy a PRISMA-guided review across major cybersecurity domains (intrusion detection, malware analysis, IoT security), develop a taxonomy of GAN applications, and provide a roadmap for future work addressing training instability, benchmarking, and explainability.
From Texts to Shields: Convergence of Large Language Models and Cybersecurity (Tao Li, Ya-Ting Yang, Yunian Pan, Quanyan Zhu): This interdisciplinary report explores the integration of large language models (LLMs) into cybersecurity workflows. It focuses on how LLMs are applied to network and software security, vulnerability analysis, and generative security engineering. The work also discusses socio-technical challenges — such as interpretability, safety, and ethical deployment — and proposes strategies like human-in-the-loop oversight and proactive robustness testing to mitigate risks.
A decade of cybersecurity research in internal auditing: bibliometric mapping and future research agenda fromDiscover Sustainability (Springer): This bibliometric study maps nearly 4,000 Scopus-indexed cybersecurity research outputs related to internal auditing over ten years. It identifies publication trends, key authors and institutions, evolving thematic clusters, and citation dynamics. The article highlights areas of concentrated research activity and proposes future directions to strengthen cybersecurity integration within internal audit practice and governance frameworks.
Building a Cybersecurity Culture in Higher Education: Proposing a Cybersecurity Awareness Paradigm (Reismary Armas & Hamed Taherdoost): Recognizing the human factor as a critical cybersecurity frontier, this research proposes a structured paradigm for cultivating cybersecurity awareness in higher education institutions. The authors analyze current gaps in awareness, outline key cultural and behavioural components, and recommend institutional strategies to embed sustainable cybersecurity practices among students, faculty, and administrative stakeholders.