CYBER THREAT DETECTION IN EVENT PROFILES VIA ARTIFICIAL NEURAL NETWORKS

Gomasani. Ramanaiah,  Dr.P.Pedda Sadhu Naik

doi:https://doi.org/10.5281/zenodo.22023398

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CYBER THREAT DETECTION IN EVENT PROFILES VIA ARTIFICIAL NEURAL NETWORKS

Gomasani. Ramanaiah, Dr.P.Pedda Sadhu Naik

DOI: https://doi.org/10.5281/zenodo.22023398

Year: 2026 | Volume: 26 | Issue: 8

Date of Publication: 2026/08/20
Keywords: Cyber security, Threat Intelligence, Logistic Regression, Decision Tree, KNN, Machine Learning, Anomaly Detection.

Abstract

With the increasing complexity of cyber threats, traditional security systems are becoming less effective in identifying new and evolving attacks. This project presents an AI-integrated adaptive threat intelligence framework that utilizes simple yet effective machine learning algorithms such as Logistic Regression, Decision Tree, and K-Nearest Neighbours (KNN) for threat detection. The system analyzes system activity and network behaviour to identify anomalies and classify them as normal or malicious. By incorporating adaptive learning, the framework continuously improves its detection capability. The proposed approach is efficient, easy to implement, and suitable for real-time cybersecurity applications, making it a practical solution for modern cybersecurity challenges.

References

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