https://github.com/ayoubelmortaji/analysis-and-detection-of-threats-in-cloud-environments-with-machine-learning

Developed a machine learning-driven threat detection model for cloud environments, utilizing Random Forest (RF) and Decision Tree (DT) algorithms. Focused on analyzing network activities and identifying suspicious behaviors through Security Information and Event Management (SIEM) systems. Enhanced cloud security by addressing key challenges in dete
https://github.com/ayoubelmortaji/analysis-and-detection-of-threats-in-cloud-environments-with-machine-learning

cloud-computing machine-learning matplotlib numpy pandas python scikit-learn seaborn siem

Last synced: 2 days ago
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Developed a machine learning-driven threat detection model for cloud environments, utilizing Random Forest (RF) and Decision Tree (DT) algorithms. Focused on analyzing network activities and identifying suspicious behaviors through Security Information and Event Management (SIEM) systems. Enhanced cloud security by addressing key challenges in dete

• Host: GitHub
• URL: https://github.com/ayoubelmortaji/analysis-and-detection-of-threats-in-cloud-environments-with-machine-learning
• Owner: AyoubElmortaji
• Created: 2025-01-27T20:28:47.000Z (7 days ago)
• Default Branch: main
• Last Pushed: 2025-01-27T20:33:52.000Z (7 days ago)
• Last Synced: 2025-01-27T21:33:30.305Z (7 days ago)
• Topics: cloud-computing, machine-learning, matplotlib, numpy, pandas, python, scikit-learn, seaborn, siem
• Homepage:
• Size: 7.81 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 📌 Analysis and Detection of Threats in Cloud Environments with Machine Learning



  


  🔍 Description : 

  


Cloud computing (CC) is a transformative technology that allows on-demand access 

to network and computing resources, such as storage and data management services, based 

on a "Pay as you go" model. It enhances system efficiency and scalability. However, 

despite its many advantages, cloud providers face significant security challenges, 

particularly when it comes to safeguarding cloud environments and services. Ensuring the 

security of these environments is critical, as vulnerabilities can expose sensitive data and 

systems to potential threats. To address these concerns, various solutions, including 

advanced monitoring systems, have been implemented to improve cloud security by 

analyzing resources, services, and network activities to detect suspicious behaviors. In this 

context, Security Information and Event Management (SIEM) systems are integral for 

monitoring network traffic and identifying anomalies that may indicate potential threats. 

This mini-project proposes a cloud-based threat detection model that leverages machine 

learning algorithms, specifically Random Forest (RF) and Decision Tree (DT). The RF 

classifier is incorporated to enhance the accuracy (ACC) of the detection model. The 

effectiveness of the proposed approach has been evaluated using two datasets, achieving an 

accuracy of 86.4% with the Decision Tree and 100% with the Random Forest classifier, 

demonstrating the potential of Machine Learning in detecting and mitigating threats in 

real-time cloud environments.