TY - JOUR AU - Elhadi, Ammar Ahmed E. AU - Maarof, Mohd Aizaini AU - Osman, Ahmed Hamza PY - 2012 TI - Malware Detection Based on Hybrid Signature Behaviour Application Programming Interface Call Graph JF - American Journal of Applied Sciences VL - 9 IS - 3 DO - 10.3844/ajassp.2012.283.288 UR - https://thescipub.com/abstract/ajassp.2012.283.288 AB - Problem statement: A malware is a program that has malicious intent. Nowadays, malware authors apply several sophisticated techniques such as packing and obfuscation to avoid malware detection. That makes zero-day attacks and false positives the most challenging problems in the malware detection field. Approach: In this study, the static and dynamic analysis techniques that are used in malware detection are surveyed. Static analysis techniques, dynamic analysis techniques and their combination including Signature-Based and Behaviour-Based techniques are discussed. Results: In addition, a new malware detection framework is proposed. Conclusion: The proposed framework combines Signature-Based with Behaviour-Based using API graph system. The goal of the proposed framework is to improve accuracy and scan process time for malware detection.