With the rapid development of the Internet, malware became one of the major cyber threats nowadays. Any software performing malicious actions, including information stealing, espionage, etc. can be referred to as malware. Kaspersky Labs (2017) define malware as “a type of program designed to infect a legitimate user's computer, mobile and inflict harm on it in multiple ways.” While the diversity of malware is increasing, anti-virus scanners cannot fulfill the needs of protection, resulting in millions of hosts being attacked. According to Kaspersky Labs (2016), 6 563 145 different hosts were attacked, and 4 000 000 unique malware objects were detected in 2015. In turn, Juniper Research (2016) predicts the cost of data breaches to increase to $2.1 trillion globally by 2019. In addition to that, there is a decrease in the skill level that is required for malware development, due to the high availability of attacking tools on the Internet nowadays. High availability of anti-detection techniques, as well as ability to buy malware on the black market result in the opportunity to become an attacker for anyone, not depending on the skill level. Current studies show that more and more attacks are being issued by script-kiddies or are automated. (Aliyev 2010). Therefore, malware protection of computer, mobile systems is one of the most important cybersecurity tasks for single users and businesses, since even a single attack can result in compromised data and sufficient losses. Massive losses and frequent attacks dictate the need for accurate and timely detection methods. Current static and dynamic methods do not provide efficient detection, especially when dealing with zero-day attacks. For this reason, machine learning-based techniques can be used. This paper discusses the main points and concerns of machine learning-based malware detection, as well as looks for the best feature representation and classification methods. The goal of this project is to develop the proof of concept for the machine learning based malware classification based on Cuckoo Sandbox. This sandbox will be utilized for the extraction of the behavior of the malware samples, which will be used as an input to the machine learning algorithms. The goal is to 6 determine the best feature representation method and how the features should be extracted, the most accurate algorithm that can distinguish the malware families with the lowest error rate. The accuracy will be measured both for the case of detection of wheher the file is malicious and for the case of classification of the file to the malware family. The accuracy of the obtained results will also be assessed in relation to current scoring implemented in Cuckoo Sandbox, and the decision of which method performs better will be made. The study conducted will allow building an additional detection module to Cuckoo Sandbox.
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