Detection of Threats using Machine Learning Techniques

Abstract

The rapid increase in cyberattacks poses a significant threat to digital infrastructure, highlighting the need for intelligent and timely threat detection systems. This paper presents a machine learning-based approach for classifying cybersecurity threats using network traffic data. Various supervised learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost, are applied and evaluated for their performance in detecting and categorizing network-based attacks. The system processes key features such as packet length, port usage, and protocol behavior to identify malicious patterns and determine attack types. A user-friendly interface is developed using Streamlit to support data upload, preprocessing, prediction, and visualization. Experimental results demonstrate that the proposed models, particularly ensemble methods, provide high accuracy in threat classification, offering an effective and scalable solution for proactive cybersecurity defense

Introduction

In today’s digital era, cyber threats are increasingly complex and frequent, challenging traditional intrusion detection systems (IDS) that rely on static rules and signatures. To overcome these challenges, integrating machine learning (ML) techniques into cybersecurity offers a more adaptive and accurate approach for detecting and classifying threats.

This paper proposes a two-stage ML-based system that uses structured network traffic features to first detect whether activity is malicious (binary classification) and then identify the specific type of attack (multiclass classification) such as DDoS, PortScan, or Ransomware. Several ML algorithms—Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost—are implemented and evaluated using standard metrics.

A user-friendly web interface built with Streamlit allows easy dataset upload, preprocessing, prediction, and visualization, making the system accessible for both researchers and cybersecurity professionals. The study shows that ensemble models like Random Forest and XGBoost perform best in classifying cyber threats.

The system addresses the limitations of traditional IDS by offering adaptability, detailed threat categorization, and modularity for future upgrades, such as real-time monitoring and integration with automated response tools. It is implemented in Python using widely adopted libraries, emphasizing usability, scalability, and practical deployment in real-world cyber defense scenarios.

Conclusion

This project presents a machine learning-based approach for classifying cyber threats using structured network traffic data. By implementing a two-stage classification process—first identifying whether the traffic is malicious, and then categorizing the type of attack—the system improves the accuracy and reliability of threat detection. Machine learning algorithms such as Random Forest, Support Vector Machine, Logistic Regression, and XGBoost were evaluated for performance and integrated into a Streamlit-based interface for ease of use. The results demonstrate that machine learning models can effectively enhance cybersecurity measures, offering a scalable and accessible solution for detecting a wide range of network threats.

References

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Copyright

Copyright © 2025 Ramesh Uma Sankar, Challa Narasimham. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.