Abstract
The need to detect malware before it harms computers, mobile phones and other electronic devices has caught the attention of researchers and the anti-malware industry for many years. To protect users from malware attacks, anti-virus software products are downloaded on the computer. The anti-virus mainly uses signature-based techniques to detect malware. However, this technique fails to detect malware that uses packing, encryption or obfuscation techniques. It also fails to detect unseen (new) ones. This paper proposes an integrated malware detection approach that applies memory forensics to extract malicious artifacts from memory and combines them to features extracted during the execution of malware in a dynamic analysis. Pre-modeling techniques were also applied for feature engineering before training and testing the data set on the machine learning models. The experimental results show a significant improvement in both detection accuracy rate and false positive rate, 98.5% and 1.7% respectively, by applying the support vector machine. The results verify that our integrated analysis approach outperforms other analysis methods. In addition, the proposed approach overcomes the limitation of single path file execution in dynamic analysis by adding more relevant memory artifacts that can reveal the real intention of malicious files.