Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

332 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A survey of multiple classifier systems as hybrid systems (2014, neutral): “ven in [183].”
A Survey on Malware Detection Using Data Mining Techniques (2017, methodology): “In recent years, a few research efforts have been conducted on surveys of data-miningbased malware detection methods [Idika and Mathur 2007; Siddiqui et al. 2008; Shabtai et al. 2009; Egele et al. 2012; Mathur and Hiranwal 2013; Bazrafshan et al. 2013; Damshenas et al. 2013; Gandotra et al. 2014].”
“Andromaly”: a behavioral malware detection framework for android devices (2012, methodology): “In these methods, the binary code of a file or the behavior of the application during execution is represented, and classifiers are used to learn patterns in order to classify new (unknown) applications as malicious or benign (Moskovitch et al. 2008; Shabtai et al. 2009c; Menahem et al. 2008).”