Mathematical Modeling and Clustering Framework for Cyber Threat Analysis Across Industries DOI Creative Commons
Fahim Sufi, Musleh Alsulami

Mathematics, Год журнала: 2025, Номер 13(4), С. 655 - 655

Опубликована: Фев. 17, 2025

The escalating prevalence of cyber threats across industries underscores the urgent need for robust analytical frameworks to understand their clustering, prevalence, and distribution. This study addresses challenge quantifying analyzing relationships between 95 distinct cyberattack types 29 industry sectors, leveraging a dataset 9261 entries filtered from over 1 million news articles. Existing approaches often fail capture nuanced patterns such complex datasets, justifying innovative methodologies. We present rigorous mathematical framework integrating chi-square tests, Bayesian inference, Gaussian Mixture Models (GMMs), Spectral Clustering. identifies key patterns, as 1150 Zero-Day Exploits clustered in IT Telecommunications sector, 732 Advanced Persistent Threats (APTs) Government Public Administration, Malware with posterior probability 0.287 dominating Healthcare sector. Temporal analyses reveal periodic spikes, Exploits, persistent presence Social Engineering Attacks, 1397 occurrences industries. These findings are quantified using significance scores (mean: 3.25 ± 0.7) probabilities, providing evidence industry-specific vulnerabilities. research offers actionable insights policymakers, cybersecurity professionals, organizational decision makers by equipping them data-driven understanding sector-specific risks. formulations replicable scalable, enabling organizations allocate resources effectively develop proactive defenses against emerging threats. By bridging theory real-world challenges, this delivers impactful contributions toward safeguarding critical infrastructure digital assets.

Язык: Английский

Mathematical Modeling and Clustering Framework for Cyber Threat Analysis Across Industries DOI Creative Commons
Fahim Sufi, Musleh Alsulami

Mathematics, Год журнала: 2025, Номер 13(4), С. 655 - 655

Опубликована: Фев. 17, 2025

The escalating prevalence of cyber threats across industries underscores the urgent need for robust analytical frameworks to understand their clustering, prevalence, and distribution. This study addresses challenge quantifying analyzing relationships between 95 distinct cyberattack types 29 industry sectors, leveraging a dataset 9261 entries filtered from over 1 million news articles. Existing approaches often fail capture nuanced patterns such complex datasets, justifying innovative methodologies. We present rigorous mathematical framework integrating chi-square tests, Bayesian inference, Gaussian Mixture Models (GMMs), Spectral Clustering. identifies key patterns, as 1150 Zero-Day Exploits clustered in IT Telecommunications sector, 732 Advanced Persistent Threats (APTs) Government Public Administration, Malware with posterior probability 0.287 dominating Healthcare sector. Temporal analyses reveal periodic spikes, Exploits, persistent presence Social Engineering Attacks, 1397 occurrences industries. These findings are quantified using significance scores (mean: 3.25 ± 0.7) probabilities, providing evidence industry-specific vulnerabilities. research offers actionable insights policymakers, cybersecurity professionals, organizational decision makers by equipping them data-driven understanding sector-specific risks. formulations replicable scalable, enabling organizations allocate resources effectively develop proactive defenses against emerging threats. By bridging theory real-world challenges, this delivers impactful contributions toward safeguarding critical infrastructure digital assets.

Язык: Английский

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