The objective of this project is to design a hybrid malware detection system that combines signature-based and behavior-based detection techniques. The system enhances detection accuracy by identifying both known malware through signatures and unknown threats through abnormal behavioral analysis.
Study traditional signature-based and modern behavior-based malware detection methods.
Analyze limitations of single-approach detection systems.
Design a system architecture integrating both signature database and behavior monitoring modules.
Develop a signature scanning module using file hashing and pattern matching.
Implement a behavior monitoring module to track suspicious system activities such as unusual CPU usage or unauthorized file modifications.
Create a centralized detection engine that correlates outputs from both modules.
Assign risk scores based on combined detection results.
Develop alert notifications and reporting dashboards.
Test the system using known malware samples and simulated unknown threats.
Evaluate detection rate improvements compared to standalone detection techniques.
Document system performance, advantages, and implementation challenges.
