The critical need for transparent and trustworthy machine learning in cybersecurity operations drives the development of this integrated Explainable AI (XAI) framework. Our methodology addresses three fundamental challenges in deploying AI for threat detection: handling massive datasets through Strategic Sampling Methodology that preserves class distributions while enabling efficient model development; ensuring experimental rigor via Automated Data Leakage Prevention that systematically identifies and removes contaminated features; and providing operational transparency through Integrated XAI Implementation using SHAP analysis for model-agnostic interpretability across algorithms. Applied to the CIC-IDS2017 dataset, our approach maintains detection efficacy while reducing computational overhead and delivering actionable explanations for security analysts. The framework demonstrates that explainability, computational efficiency, and experimental integrity can be simultaneously achieved, providing a robust foundation for deploying trustworthy AI systems in security operations centers where decision transparency is paramount.
Cybersecurity is now at the forefront of most organisational digital transformative agendas and National economic, social and political programmes. Hence its impact to society can no longer be seen to be one dimensional. The rise in National cybersecurity laws and regulations is a good indicator of its perceived importance to nations. And the recent awakening for social and ethical transparency in society and coupled with sustainability issues demonstrate the need for a paradigm shift in how cybersecurity discourses can now happen. In response to this shift, a multidimensional cybersecurity fr…
Strategic Insights from Simulation Gaming of AI Race Dynamics
Wichita State University（威奇托州立大学）；Barton School of Business（巴顿商学院）；Transformative Futures Institute（变革性未来研究所）；University of Cambridge（剑桥大学）；Centre for the Study of Existential Risk（存在风险研究中心）；Centre for the Future of Intelligence（未来智能中心）；Oxford University（牛津大学）；London Initiative for Safe AI（伦敦安全人工智能倡议）；Monash University（莫纳什大学）；Monash Sustainable Development Institute（莫纳什可持续发展研究所）；BehaviourWorks Australia（澳大利亚行为研究中心）
Delft University of Technology（代尔夫特理工大学）；University of Padua（帕多瓦大学）
Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines. We systematize the increasingly growing (but fragmented) microcosm of studies that develop and utilize XAI methods for defensive and offensive cybersecurity tasks. We identify 3 cybersecurity stakeholders, i.e., model users, designers, and adversaries, who utilize XAI for 4 distinct objectives within an ML pipeline, namely 1) XAI-enabled user assistance, 2) XAI-enabled model verification, 3) explanation verification & robustness, and 4) offensive use of explanations. Our analysis of t…
Governments around the world are required to strengthen their national cybersecurity capabilities to respond effectively to the growing, changing, and sophisticated cyber threats and attacks, thus protecting society and the way of life as a whole. Responsible government institutions need to revise, evaluate, and bolster their national cybersecurity capabilities to fulfill the new requirements, for example regarding new trends affecting cybersecurity, key supporting laws and regulations, and implementations risk and challenges. This report presents a comprehensive assessment instrument for cybe…
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities
Guelma University（盖勒马大学）；Technology Innovation Institute（技术创新研究院）；Concordia University（康考迪亚大学）；University of Oslo（奥斯陆大学）；Khalifa University of Science and Technology（哈利法科技大学）
A Survey of Social Cybersecurity: Techniques for Attack Detection, Evaluations, Challenges, and Future Prospects
Saginaw Valley State University（萨吉诺谷州立大学）；Howard University（霍华德大学）；University of Cincinnati（辛辛那提大学）；University of North Texas（北得克萨斯大学）；Qatar University（卡塔尔大学）；Carnegie Mellon University（卡内基梅隆大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
The multilingual nature of the Internet increases complications in the cybersecurity community's ongoing efforts to strategically mine threat intelligence from OSINT data on the web. OSINT sources such as social media, blogs, and dark web vulnerability markets exist in diverse languages and hinder security analysts, who are unable to draw conclusions from intelligence in languages they don't understand. Although third party translation engines are growing stronger, they are unsuited for private security environments. First, sensitive intelligence is not a permitted input to third party engines…
