Improving malware detection response time with behavior-based statistical analysis techniques
Published in 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2015
Recommended citation: Prelipcean, Dumitru Bogdan and Popescu, Adrian Stefan and Gavrilut, Dragos Teodor, "Improving malware detection response time with behavior-based statistical analysis techniques." 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pages 232-239, IEEE, 2015. https://doi.org/10.1109/SYNASC.2015.42
Abstract
This paper addresses the critical challenge of reducing malware detection response time while maintaining detection accuracy. We propose behavior-based statistical analysis techniques that significantly improve the speed of malware identification in real-time systems.
Key Contributions
- Enhanced Response Time: Significant reduction in malware detection latency
- Statistical Analysis Framework: Novel behavior-based analysis techniques
- Maintained Accuracy: High detection rates preserved despite improved speed
- Real-world Validation: Tested on large-scale malware datasets
Technical Approach
The paper introduces a comprehensive framework that:
- Analyzes behavioral patterns in real-time
- Employs statistical methods to reduce false positives
- Optimizes detection algorithms for speed without sacrificing accuracy
- Implements efficient data structures for rapid pattern matching
Impact
This work has been instrumental in developing faster detection systems at Bitdefender, contributing to real-time threat protection for millions of users worldwide.