DSCI SPONSORED PROJECT
Ransomware Detection using Machine learning/Deep learning.
Ransomware attacks have become a major threat to organizations of all sizes, as they can result in the loss of important data and significant downtime. Traditional ransomware detection methods, such as signature-based detection and heuristic analysis, have proven to be inadequate in dealing with the constantly evolving nature of ransomware. In response to this challenge, the project “Deep-RW: A deep learning tool for Ransomware Detection” presents a novel approach to detect ransomware using deep learning techniques. The Deep-RW system is designed to analyze the behavior of a given program and determine whether it exhibits characteristics associated with ransomware. This is accomplished by training a deep neural network on a large dataset of known ransomware and benign programs, using a variety of features such as API calls and network traffic patterns. The network is then able to accurately identify ransomware in previously unseen programs with high precision and low false positives.
Expected Outcomes
The effectiveness of the Deep-RW system has been demonstrated through extensive experiments, in which it achieved high accuracy and outperformed traditional methods. The system is also scalable and efficient, making it suitable for use in a real-world setting
High Accuracy
A deep learning-based ransomware detection system (Deep-RW) that can accurately identify ransomware and distinguish it from benign software.
High Performance Tool
Deep-RW is the high-performance tool that performs early detection of ransomware which in-turn significantly reduces the security risk and prevent the reputation harm of industries, institutions, etc.
Web Application
The outcome of the project will be available in the form of stand-alone and/or web-based application that help the researchers and industries to predict the ransomware detection.
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Portfolio
Industry Partners
Our product is used by leading industry brands and partners.
Objectives
- To develop a deep learning-based early ransomware detection system that can accurately identify malware and distinguish it from benign software.
- To create a high-performance learning framework for ransomware detection system using multi-objective optimization method that will legitimately determine the kernels and optimal feature mapping in the ensemble formation.
- To create an application, either web-based or stand-alone, that can analyse network data for signs of ransomware and alert researchers and businesses.
- To investigate the effectiveness of different deep learning architectures and algorithms for detecting ransomware.
- To evaluate the proposed system using a large dataset of known ransomware and benign software.
Project Outcomes
- A deep learning-based ransomware detection system (Deep-RW) that can accurately identify ransomware and distinguish it from benign software.
- Deep-RW is the high-performance tool that performs early detection of ransomware which in-turn significantly reduces the security risk and prevent the reputation harm of industries, institutions, etc.
- The outcome of the project will be available in the form of stand-alone and/or web-based application that help the researchers and industries to predict the ransomware
"Amazing Product with Excellent Accuracy!"
Meet Our Team
The researchers who have been working on the project and have made it a success.
Dr. Simranjit SIngh
Professor, NIT Jalandhar
Dr. Mohit Sajwan
Professor, NSUT Delhi
Summary of roles/responsibilities for all Investigators
- Applying Deep/Machine Learning Models
- Validation of the model
- Data Collection
- Data Cleansing
- Deep Learning Model
- Feature extraction and selection
- Optimizing the overall process
- Result analysis and validation.
- Development of stand-alone application
Would you like to contribute to this project with us?
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