@article {10.3844/ajassp.2012.283.288, article_type = {journal}, title = {Malware Detection Based on Hybrid Signature Behaviour Application Programming Interface Call Graph}, author = {Elhadi, Ammar Ahmed E. and Maarof, Mohd Aizaini and Osman, Ahmed Hamza}, volume = {9}, year = {2012}, month = {Jan}, pages = {283-288}, doi = {10.3844/ajassp.2012.283.288}, url = {https://thescipub.com/abstract/ajassp.2012.283.288}, abstract = {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.}, journal = {American Journal of Applied Sciences}, publisher = {Science Publications} }