Machine Learning Detection of Ransomware by Lightweight Mini-filters
- Resource Type
- Conference
- Authors
- Chiu, Chen-Yu; Wu, Min-Hao; Huang, Jian-Hung; Chen, Jian-Xin; Wang, Hao-Jyun
- Source
- 2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI) Educational Innovation (ECEI), 2023 IEEE 6th Eurasian Conference on. :183-187 Feb, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Computers
Technological innovation
Accesslists
Machine learning
Real-time systems
Encryption
Behavioral sciences
Ransomware
minfilter
windows driver
- Language
Users are more at risk from ransomware as time goes on. Invading users' computers with ransomware aims to encrypt their data and demand payment. Although anti-virus software may identify ransomware assaults on computers, it cannot prevent them until they are identified. Since many users may have already been hit by ransomware during this viral window period, safeguarding users during this time becomes a priority. We present a way to identify suspected ransomware in real-time. It would integrate into the Windows mini-filter driver to fight against ransomware assaults. This approach makes it challenging for ransomware to evade our detection. Our technology allows consumers to terminate the currently running application or put it on the whitelist once it has been flagged as potentially malicious software. Our solution enables users to edit the software and recovers the altered files when they choose to end the application, lessening their loss.