INTEGRATING YARA FOR EFFICIENT MALWARE SCANNING IN CYBERSECURITY

Abstract: Malware continues to pose a significant threat to digital infrastructures, requiring efficient detection mechanisms that balance accuracy, scalability, and simplicity. This paper presents the design and implementation of a lightweight malware scanner built on YARA, an open-source tool widely adopted for pattern-based malware identification. The proposed scanner leverages custom YARA rules to detect malicious binaries and scripts by matching known signatures and behavioral patterns. Emphasis is placed on rule optimization to reduce false positives while maintaining detection speed. Experimental evaluation demonstrates that the scanner effectively identifies common malware families with minimal resource consumption, making it suitable for integration into endpoint security solutions and incident response workflows. By combining simplicity with extensibility, this approach highlights the practicality of YARA-based detection in academic research, enterprise environments, and security operations centers. The study concludes with recommendations for enhancing rule sets through community collaboration and integrating the techniques to strengthen resilience against evolving threats.

Keywords: Malware Detection, YARA Rules, Cybersecurity, Lightweight Scanner, Pattern-Based Identification.