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<p>This paper provides a comprehensive analysis of Black Basta, a sophisticated ransomware strain that emerged in April 2022 and rapidly gained notoriety in the cybersecurity landscape. It introduces a novel Multi-Source Intelligence Framework (MSIF) for comprehensive analysis of the Black Basta ransomware operation, combining primary honeypot data collection, reverse engineering analysis, and behavioural pattern recognition. Unlike previous descriptive studies, this research presents original empirical findings from a 12-month longitudinal study involving controlled malware analysis, network traffic examination, and victim impact assessment across 127 documented incidents. The study contributes three key innovations: a standardized ransomware analysis methodology validated against multiple threat families, an adaptive defense architecture that reduces successful encryption by 50.8% (95% CI: 37.2%-64.4%) reduction in controlled environments, and predictive models for attack vector identification with 87.4% accuracy (95% CI: 84.2%-90.6%). Through cross-validation using both public datasets and proprietary intelligence, this framework demonstrates superior performance compared to existing detection methods. The research addresses critical knowledge gaps in real-time ransomware detection, automated response mechanisms, and proactive threat mitigation. Results show the proposed framework can identify Black Basta variants 48–72&#xa0;h before traditional signature-based detection, enabling pre-emptive defensive measures.</p>

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Black Basta ransomware: a novel multi-source intelligence framework for advanced cyber threat analysis and proactive defense

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月18日

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  • Jude Osamor

摘要

This paper provides a comprehensive analysis of Black Basta, a sophisticated ransomware strain that emerged in April 2022 and rapidly gained notoriety in the cybersecurity landscape. It introduces a novel Multi-Source Intelligence Framework (MSIF) for comprehensive analysis of the Black Basta ransomware operation, combining primary honeypot data collection, reverse engineering analysis, and behavioural pattern recognition. Unlike previous descriptive studies, this research presents original empirical findings from a 12-month longitudinal study involving controlled malware analysis, network traffic examination, and victim impact assessment across 127 documented incidents. The study contributes three key innovations: a standardized ransomware analysis methodology validated against multiple threat families, an adaptive defense architecture that reduces successful encryption by 50.8% (95% CI: 37.2%-64.4%) reduction in controlled environments, and predictive models for attack vector identification with 87.4% accuracy (95% CI: 84.2%-90.6%). Through cross-validation using both public datasets and proprietary intelligence, this framework demonstrates superior performance compared to existing detection methods. The research addresses critical knowledge gaps in real-time ransomware detection, automated response mechanisms, and proactive threat mitigation. Results show the proposed framework can identify Black Basta variants 48–72 h before traditional signature-based detection, enabling pre-emptive defensive measures.