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All Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Tajudeen, Abdulquadri
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Computer Science & IT Engineering Mechanical Engineering Transportation

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A Survey on Categorization of Threat Intelligence and Trust-based Sharing Strategies on Cyber Attack Nureni, Azeez; Tajudeen, Abdulquadri
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.36031

Abstract

Threat Intelligence (TI) refers to knowledge derived from analyzing current and potential cyber threats, including their context, mechanisms, and indicators of compromise. By understanding adversaries ' tactics, techniques, and procedures, TI empowers organizations to proactively detect, prevent, and counter cyber threats. Given cyberattacks' increasing frequency and sophistication, stratifying and categorizing TI remains challenging, particularly in building trust for secure information sharing among organizations. This research addresses these challenges through a survey on TI categorization and trust-based sharing mechanisms. The study is expository researchthat employs quantitativeresearch methodology. The study incorporates a systematic literature review to explore TI classification, methodologies, and its effectiveness in mitigating cybersecurity vulnerabilities. Findings reveal that organizations leveraging advanced TI methods, such as machine learning and behavioral analytics, achieve up to a 60% reduction in threat detection and response times. Furthermore, trust-based sharing initiatives such as Information Sharing and Analysis Centers (ISACs) and standardized frameworks like Structured Threat Information eXpression (STIX) and Trusted Automated eXchange of Indicator Information (TAXII) enhance collaborative defense capabilities by 65%. The study concludes that integrating standardized sharing protocols, advanced analytics, and machine learning can significantly bolster cybersecurity defenses. It recommends global standardization of TI practices, incentivizing participation in information-sharing communities, and investing in workforce training to optimize TI deployment. These findings allow practitioners, policymakers, and researchers to strengthen cybersecurity frameworks.
Co-Authors Nureni, Azeez