Skip to content

Malware detection is a critical task in cybersecurity that involves identifying and classifying malicious software. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Notifications You must be signed in to change notification settings

Projects-Developer/Malware-Detection-Using-Machine-learning-and-Deep-Learning

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 

Repository files navigation

Malware Detection Using Machine learning and Deep Learning

Malware detection Usine Machine learning and deep learning With Code, documents and Video tutorial

Malware

Abstract:

The rapid evolution of malware has rendered traditional signature-based detection methods ineffective. Machine learning (ML) and deep learning (DL) have emerged as promising solutions for detecting malware. This paper provides an overview of malware detection using ML and DL techniques. We discuss the advantages and challenges of using ML and DL for malware detection, and highlight real-world applications. Our goal is to provide a comprehensive understanding of the role of ML and DL in malware detection.

keywords: Malware detection, Machine learning, Deep learning, Cybersecurity, Artificial intelligence, Threat detection, Anomaly detection

Project include:

  1. Synopsis

  2. PPT

  3. Research Paper

  4. Code

  5. Explanation video

  6. Documents

  7. Report

Need Code, Documents & Explanation video ?

How to Reach me :

WhatsApp: +91 9310631437 (Helping 24*7) CHAT

Contact me for any kind of help on projects.

1000 Computer Science Projects : https://www.computer-science-project.in/

Mail/Message me for Projects Help 🙏🏻

About

Malware detection is a critical task in cybersecurity that involves identifying and classifying malicious software. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published