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Malware detection and evasion with machine learning techniques: A survey

Abstract:

Malware has become a powerful and sophisticated tool used by malicious users to compromise and harm systems, and its evasion ability has improved considerably, getting to the point of becoming completely undetectable. On the other hand, machine learning has evolved tremendously in last years and it has become a standard in many IT solutions including the data processing field. Likewise, cryptography also has growth in popularity in providing confidentiality and integrity to important information. Even though those technologies are being widely used for trustable IT solutions, they also are used by malicious applications such as ransomware, which uses the cryptography as its infecting mechanism and the machine learning as its evasion technique. In this aspect, this paper makes a survey of existing researches regarding to malware detection and evasion by examining possible scenarios where malware could take advantage of machine learning and cryptography to improve its evasion techniques and infection impact.