About The Project
DeepRW
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.
Why DeepRW
The All-India Institute of Medical Sciences (AIIMS) has not updated its computer and IT systems for the last 30 years according to Times of India. The hospital was recently hit by a ransomware attack that compromised the medical records of millions of patients, including VIPs.
The outdated equipment and software, as well as the use of old versions of the Windows operating system, contributed to the vulnerability of the hospital’s IT systems.
The hospital administration is now planning to create a cybersecurity policy and hire IT professionals to improve the safety of its systems and patient data.
Key Contributions
- Our research indicates that the proposed toolkit is the first comprehensive framework capable of early ransomware detection.
- In order to enhance ransomware detection, the proposed detection framework makes use of a number of different feature-kernel pairs and a near-optimal weights assignment achieved via multi-objective optimization.
- 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 malicious ransomware activities.
Our Conclusion
- In this research proposal, we propose to develop and evaluate a deep learning-based ransomware detection system. We will investigate the effectiveness of different deep learning architectures and algorithms for detecting ransomware and compare the proposed system with existing approaches.
- In conclusion, deep learning is a promising approach for ransomware analysis and detection. Its ability to learn complex patterns in data and adapt to new variants of ransomware makes it a valuable tool for detecting and mitigating ransomware attacks.
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
Our 6-D Process
01.
Discover
02.
Define
03.
Design
04.
Develop
05.
Deploy
06.
Deliver
The utilization of deep learning for ransomware analysis has several advantages over traditional methods. First, deep learning algorithms can learn complex patterns in data and can be trained on large amounts of data, which makes them more effective at detecting ransomware than signature-based antivirus software. Second, because of their flexibility, deep learning algorithms can be updated to deal with the ever-evolving threat posed by ransomware. Because of this, they are better able to withstand the constantly shifting ransomware landscape.
DeepRW Methodology
Deep learning is a promising approach for ransomware analysis and detection. Its ability to learn complex patterns in data and adapt to new variants of ransomware makes it a valuable tool for detecting and mitigating ransomware attacks. Further research in this area could lead to the development of more effective ransomware analysis and detection systems.
Collection and preparation of a large dataset of known ransomware and benign software samples. This will involve obtaining samples from various sources, such as public repositories and malware collections, and pre-processing the samples to extract features that can be used as input to the deep learning model.
Data pre-processing is an essential step in the process of developing a machine learning model is shown in Figure 1. It involves preparing the raw data so that it can be effectively used in the model-building process. Normalization and standardization are two common techniques used in data pre-processing to ensure that the data is in a format that can be easily understood and used by the machine/deep learning algorithms.
Designing a ransomware detection system that makes use of several different deep learning architectures and methods. This will entail putting the model through its paces on the ready-made dataset and checking how well it performed using various metrics including accuracy, precision, and recall. In Figure 2 we see the training of the model in action. The subsequent process is to adjust the parameters of the deep learning model.
Evaluation and Comparison of the proposed system with existing ransomware detection approaches, such as signature-based detection and machine learning-based detection. This will involve applying the existing approaches to the same dataset and comparing their performance with that of the proposed system.
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