Abstract :

In The Rapidly Evolving Landscape Of Cybersecurity, Timely And Efficient Sharing Of Threat Intelligence Is Crucial For Organizations To Defend Against Sophisticated Cyber-attacks. This Project Focuses On The Implementation Of A Threat Intelligence Sharing Platform Using The Malware Information Sharing Platform (MISP), An Open-source Tool Designed To Facilitate The Collection, Sharing, And Analysis Of Cyber Threat Data. By Leveraging MISP, Organizations Can Collaboratively Share Indicators Of Compromise (IOCs), Attack Patterns, And Threat Actor Profiles To Improve Overall Situational Awareness And Response Capabilities.To Enhance The Effectiveness Of The Platform, This Project Integrates Machine Learning Techniques To Automate The Detection, Classification, And Prioritization Of Threat Data Shared Within The MISP Ecosystem. Machine Learning Models Analyze Vast Amounts Of Threat Intelligence To Identify Patterns And Anomalies That Might Indicate Emerging Threats Or False Positives, Thereby Improving The Accuracy And Speed Of Threat Detection. This Approach Aims To Reduce The Manual Effort Required For Threat Analysis And Enables Proactive Defense Strategies.The Implementation Includes Developing Data Ingestion Pipelines To Normalize And Preprocess Diverse Threat Intelligence Formats, Ensuring Seamless Integration With Machine Learning Modules. Various Supervised And Unsupervised Learning Algorithms Are Explored For Tasks Such As Threat Classification, Clustering Similar Incidents, And Predicting Attack Trends. The Platform Also Supports Continuous Learning, Allowing Models To Evolve As New Threat Data Becomes Available, Thereby Maintaining High Detection Efficacy Over Time.

Published:

24-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

898-905

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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