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Malware presents a major threat to the security of computer systems, smart devices, and applications. It can also endanger sensitive data by modifying or destroying them. Thus, electronic exchanges through different communicating entities can be compromised. However, currently used signature-based methods cannot provide accurate detection of zero-day attacks, polymorphic and metamorphic programs which have the ability to change their code during propagation. In order to solve this issue, static and dynamic malware analysis is being used along with machine learning algorithms for malware detection and classification. Machine learning methods play an important role in automated malware detection. Several approaches have been applied to classify and to detect malware. The most challenging task is selecting a relevant set of features from a large dataset so that the classification model can be built in less time with higher accuracy. The purpose of this work is firstly to make a general review on the existing classification and detection methods, and secondly to develop an automated system to detect malicious Portable Executable files based on their headers with low performance and more efficiency. Experimental results will be presented for the best classifier selected in this study, namely Random Forest; accuracy and time performance will be discussed.

A New Classification Based Model for Malicious PE Files Detection