The escalating sophistication of cyberattacks, exemplified by supply chain compromises, AI-driven obfuscation, and politically motivated campaigns, makes accurate attribution a critical yet elusive challenge for national security and economic stability. The inability to reliably trace attacks to their source undermines deterrence, distorts policy responses, and erodes trust in digital ecosystems. Traditional methods struggle with the sheer volume of digital evidence, rapidly evolving adversary tactics, and the inherent complexities of cross-border operations. Moreover, existing literature often provides fragmented analyses, focuses narrowly on cyber threat intelligence sharing or specific threat types, or predates significant advancements in AI/ML tailored for attribution. This survey offers a comprehensive, interdisciplinary review of cyber threat attribution, bridging these critical gaps by systematically analyzing its multifaceted dimensions: technical, legal, geopolitical, social, and economic. Employing a rigorous, PRISMA-ScR compliant methodology that included structured screening and quality assessment across six major databases, we critically appraise current techniques and identify a paradigm shift toward data-driven, intelligent approaches. A key contribution is our novel taxonomy, which structures attribution research by attribution confidence & granularity (the Level of attribution), analytical domains (the “How” and “Where” of evidence processing) and adversarial motivation & profile (the “Why” and “Who”), providing a crucial framework for systematic cross-study comparisons in a complex field. Our findings underscore the transformative potential of emerging AI/ML techniques, particularly graph neural networks, in automating analysis, identifying subtle patterns, and extracting crucial insights from vast datasets, thereby revolutionizing attribution accuracy. This research provides actionable insights for practitioners and policymakers, offering a comprehensive roadmap to advance cyber defense and foster a more resilient and secure global digital ecosystem.

Generative Artificial Intelligence (GenAI) is transforming the workplace, but its adoption introduces significant risks to data security and privacy. Recent incidents underscore the urgency of addressing these issues. This comprehensive survey investigates the implications of GenAI integration in workplaces, focusing on its impact on organizational operations and security. We analyze vulnerabilities within GenAI systems, threats they face, and repercussions of AI-driven workplace monitoring. By examining diverse attack vectors like model attacks and automated cyberattacks, we expose their potential to undermine data integrity and privacy. Unlike previous works, this survey specifically focuses on the security and privacy implications of GenAI within workplace settings, addressing issues like employee monitoring, deepfakes , and regulatory compliance. We delve into emerging threats during model training and usage phases, proposing countermeasures such as differential privacy for training data and robust authentication for access control. Additionally, we provide a comprehensive analysis of evolving regulatory frameworks governing AI tools globally. Based on our comprehensive analysis, we propose targeted recommendations for future research and policy-making to promote responsible and secure adoption of GenAI in the workplace, such as incentivizing the development of explainable AI (XAI) and establishing clear guidelines for ethical data usage. This survey equips stakeholders with a comprehensive understanding of GenAI’s complex workplace landscape, empowering them to harness its benefits responsibly while mitigating risks.

Space anomaly detection plays a critical role in safeguarding the integrity and reliability of space systems amid the rising tide of threats. This survey aims to deepen comprehension of space cyber threats through space threat modeling, and meticulously examine the unique challenges of space anomaly detection. The survey identifies scalability, real-time detection, limited labeled data availability, concept drift, and adversarial attacks as key challenges based on thorough literature analysis and synthesis. By extensively exploring state-of-the-art anomaly detection techniques, the study evaluates their applicability, strengths, and limitations within space networks. Going beyond analysis, a notable contribution of this work involves integrating stream-based and graph-based methods, tailored to capture the intricate temporal and structural relationships inherent in space networks. This innovative hybrid approach holds promise for heightened detection accuracy and sets the stage for future research endeavors. As space threats continue evolving in both number and sophistication, this survey timely provides insights, recommendations, and a clear roadmap for researchers, engineers, and practitioners to fortify space anomaly detection mechanisms.

Identity sharing systems, regardless of their architectural models, share common vulnerabilities. These systems compel users to divulge personal information and furnish proof of identity for accessing services, leaving them susceptible to data breaches that can culminate in identity theft and jeopardize online data security. While blockchain technology offers a potential remedy, delivering enhanced security, immutability, and traceability, it simultaneously raises pertinent concerns surrounding privacy and transparency. The integration of zero-knowledge proof (ZKP) technology has emerged as a promising solution, particularly in enhancing privacy within the transparent blockchain ecosystem. Our paper conducts an exhaustive survey of the existing literature, with a particular focus on the assimilation of ZKP technology into blockchain for the secure sharing of user identities. We undertake a critical evaluation of the advancements achieved in this domain, pinpoint the formidable challenges that must be confronted, and uncover nascent opportunities for further exploration. Our contribution transcends the realms of mere summarization and analysis; we go a step further by offering recommendations drawn from real-world case studies and delineating future research directions.