https://github.com/projects-developer/malware-detection-using-machine-learning-and-deep-learning-

Malware poses a significant threat to computer systems, networks, and sensitive data.
https://github.com/projects-developer/malware-detection-using-machine-learning-and-deep-learning-

deeplearning deeplearningformalwaredetection machinelearning malwareanalysis malwaredetection mlforcybersecurity

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Malware poses a significant threat to computer systems, networks, and sensitive data.

• Host: GitHub
• URL: https://github.com/projects-developer/malware-detection-using-machine-learning-and-deep-learning-
• Owner: Projects-Developer
• Created: 2024-12-21T09:03:16.000Z (19 days ago)
• Default Branch: main
• Last Pushed: 2024-12-21T09:19:55.000Z (19 days ago)
• Last Synced: 2024-12-21T10:19:59.932Z (19 days ago)
• Topics: deeplearning, deeplearningformalwaredetection, machinelearning, malwareanalysis, malwaredetection, mlforcybersecurity
• Homepage: https://www.finalproject.in/
• Size: 3.91 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Malware-Detection-using-Machine-learning-and-Deep-Learning-

Malware Detection using Machine learning and Deep Learning Code, Document And Vidoe Tutorial

![malware analysis](https://github.com/user-attachments/assets/fffd56c5-54c0-43e9-b8f5-e2dac40841dc)

## Youtube link: https://youtu.be/LdhoFN2ZTFM?si=FL8nF7f3MCjyXC1G

## Abstract:

Malware poses a significant threat to computer systems, networks, and sensitive data. Traditional detection methods are ineffective against zero-day attacks and evolving malware variants. This study proposes a hybrid approach combining machine learning (ML) and deep learning (DL) techniques for detecting malware. The proposed system utilizes ML models such as Random Forest and Support Vector Machine, and DL models such as Convolutional Neural Networks and Recurrent Neural Networks. The hybrid approach achieves improved detection accuracy, reduces false positives, and detects zero-day attacks and unknown malware variants. The proposed system has the potential to provide real-time detection and alerts, enabling prompt action against malware threats.

Keywords: Malware Detection, Machine Learning, Deep Learning, Hybrid Approach, Zero-Day Attacks, Real-Time Detection.

### Project include: 

1. Synopsis

2. PPT

3. Research Paper

4. Code

5. Explanation video

6. Documents

7. Report

### Need Code, Documents & Explanation video ? 

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