Vol. 1 No. 1 (2026): January 2026
Artificial Intelligence : Technology

Understanding the Evolving Landscape of Malware Threats Through Cyber Threat Intelligence

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  • D Nagabhushanam,
  • P S Usha Rani,
  • P Gowthami,
  • S Mohammed Sameer,
  • K Yuvaraj,
  • M Balaji
D Nagabhushanam
Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, Andra Pradesh, India.
P S Usha Rani
Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, Andra Pradesh, India.
P Gowthami
Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, Andra Pradesh, India.
S Mohammed Sameer
Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, Andra Pradesh, India.
K Yuvaraj
Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, Andra Pradesh, India.
M Balaji
Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, Andra Pradesh, India.
DOI: https://doi.org/10.5281/zenodo.18525909

Published 2026-02-08

Keywords

  • Malware Detection,
  • Cyber Threat Intelligence,
  • Machine Learning,
  • Random Forest,
  • Cybersecurity,
  • Threat Analysis
    • ...More
    Less

How to Cite

D Nagabhushanam, P S Usha Rani, P Gowthami, S Mohammed Sameer, K Yuvaraj, & M Balaji. (2026). Understanding the Evolving Landscape of Malware Threats Through Cyber Threat Intelligence. Milestone Transactions on Artificial Intelligence, 1(1), 78–91. https://doi.org/10.5281/zenodo.18525909

Abstract

The rapid development of sophisticated forms of malware and ever-changing cyber threats have become a major challenge for cybersecurity globally. Using cyber threat intelligence (CTI), this study aims to provide an overall analysis of the current trend of malware. It has identified various forms of recent attack patterns, behaviors, and evasion techniques of malware. The study has explored various forms of malware activation, propagation, and evasion techniques. It has evaluated their impact on critical infrastructure such as finance, healthcare, and other sectors. Using various case studies, experts, and threat intelligence, this study has demonstrated the importance of timely and accurate threat analysis. A comparative study of various machine learning-based threat classification has been implemented with RF, SVM, and DT algorithms. The performance evaluation of these classifiers shows that the RF classifier performs better than the others. It has achieved an accuracy of 95.57%. Hence, it has been able to show its efficiency while dealing with problems that involve a large number of dimensions, such as cybersecurity. The importance of intelligent detection and mitigation techniques in dealing with malware attacks has been revealed. The importance of international cooperation and collaboration in dealing with malware threats has been demonstrated.

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