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Machine Learning to Predict the Likelihood of a Personal Computer to Be Infected with Malware Maryam Shahini Southern Methodist University, [email protected]

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Machine Learning to Predict the Likelihood of a Personal Computer to Be Infected with Malware Maryam Shahini Southern Methodist University, Mshahini@Smu.Edu SMU Data Science Review Volume 2 | Number 2 Article 9 2019 Machine Learning to Predict the Likelihood of a Personal Computer to Be Infected with Malware Maryam Shahini Southern Methodist University, [email protected] Ramin Farhanian Southern Methodist University, [email protected] Marcus Ellis Northeastern University, [email protected] Follow this and additional works at: https://scholar.smu.edu/datasciencereview Part of the Computer and Systems Architecture Commons Recommended Citation Shahini, Maryam; Farhanian, Ramin; and Ellis, Marcus (2019) "Machine Learning to Predict the Likelihood of a Personal Computer to Be Infected with Malware," SMU Data Science Review: Vol. 2 : No. 2 , Article 9. Available at: https://scholar.smu.edu/datasciencereview/vol2/iss2/9 This Article is brought to you for free and open access by SMU Scholar. It has been accepted for inclusion in SMU Data Science Review by an authorized administrator of SMU Scholar. For more information, please visit http://digitalrepository.smu.edu. Shahini et al.: Likelihood of a Personal Computer to Be Infected with Malware Machine Learning to Predict the Likelihood of a Personal Computer to Be Infected with Malware Maryam Shahini, Ramin Farhanian, Marcus Ellis Master of Science in Data Science Southern Methodist University Dallas, Texas USA fmshahini, [email protected], [email protected] Abstract. In this paper, we present a new model to predict the prob- ability that a personal computer will become infected with malware. The dataset is selected from a Kaggle competition supported by Mi- crosoft. The data includes computer configuration, owner information, installed software, and configuration information. In our research, sev- eral classification models are utilized to assign a probability of a machine being infected with malware. The LightGBM classifier is the optimum machine learning model by performing faster with higher efficiency and lower memory usage in this research. The LightGBM algorithm obtained a cross-validation ROC-AUC score of 74%. Leading factors and feature importance are also identified by LightGBM technique. Our research revealed that variables related to location, firmware version, operating system, and anti-virus software are the most important variables that have the highest weight in predicting malware detection. 1 Introduction Malware is a malicious software that is designed to damage or disable computers. Once a computer is infected by malware, criminals can hurt consumers and enterprises, cause damage, and steal private information without the consent of the user. It can significantly impact a computers performance in many different ways such as disrupting network connection and operations, installing additional software and switching computer settings. In todays' technology-driven world, we do not have the tools to predict the probability of a malware infection before it happens. To solve this problem, It is imperative to identify the factors that increase the risk of malware infection and take precautions necessary to prevent infections. With more than one billion enterprises and consumer customers, Microsoft takes the malware infection problem seriously and is deeply invested in improv- ing the security of its platform. This company is challenging the data science community to develop techniques to predict malware infection. As with their previous Malware Challenge (2015)1, Microsoft has provided a malware dataset 1 Microsoft Malware Classification Challenge. [online] Available at: https://www. kaggle.com/c/malware-classification Published by SMU Scholar, 2019 1 SMU Data Science Review, Vol. 2 [2019], No. 2, Art. 9 to encourage implementation of an effective technique to predict malware occur- rences. The dataset is provided by a Kaggle competition supported by Microsoft containing different machines' properties. The machine infections were generated by combining heartbeat and threat reports collected by Microsoft's endpoint protection solution, Windows Defender. We present a machine learning model in an efficient way to avoid malware security issues for Microsoft clients before they happen. The first step in the process is to perform exploratory data analysis to understand the patterns and remove the unnecessary variables from our analysis[1]. Exploratory data analysis is done by eliminating variables with a high percentage of missing observations and suspected features. As a result, the dataset was left with 57 variables to use in the model[15]. Identifying the right machine learning algorithm to perform an analysis on Microsoft data is the next step. Many attempts are made with various algo- rithms; however, LightGBM has been determined to be the best for this applica- tion. The main advantages are faster training speed, high efficiency, scalability, higher accuracy, and lower memory usage. The ROC-AUC score of 74% is the best result that we have achieved . LightGBM has also helped us identify specific characteristics and proper- ties of the machine that have the higher weight in malware infection predic- tion. Some of the most important features are the \CityIdentifier”2 and \Cen- sus FirmwareVersionIdentifier”(The version id of the firmware3). In the following section, a background of cybercrime is presented. Then we introduce a discussion on cybersecurity in section 3. Section 4 discusses more details regarding our collected data, steps in data preparation and initial insight. A description of different evaluation metrics is provided in section 5. Section 6 is dedicated to our optimum model and results. Ethical issues are addressed in section 7. Finally, the conclusions of our work and the main points of evidence are summarized in section 8. 2 Cybercrime The proliferation of digital technology, and the convergence of computing and communication devices, has transformed the way in which we socialize and do business. While these aspects of digital technology are very positive, there has also been a dark side to these developments. Virtually every advance has been accompanied by a corresponding niche to be exploited for criminal purposes[4]. The magic of digital cameras and sharing photos on the Internet is exploited by child pornographers. Electronic banking and online sales have provided fertile 2 the individual ID for the city that the machine is located in 3 Firmware is a layer of software between hardware and the operating system with the main purpose to initialize and abstract enough hardware so that the operating systems and their drivers can further configure the hardware to its full functional- ity[19]. https://scholar.smu.edu/datasciencereview/vol2/iss2/9 2 Shahini et al.: Likelihood of a Personal Computer to Be Infected with Malware ground for fraud. Electronic communication such as email and text messaging may be used to stalk and harass. Our increasing dependence on computers and digital networks makes technology a tempting target for gaining information or as a means of causing disruption and damage. The idea of a separate category of computer crime arose about the same time that computers became more mainstream in the society. As early as the 1960s there were reports of computer manipulation, sabotage, computer espionage and the illegal use of computer systems. The 1970s saw the first serious treatments of \computer crime". In subsequent decades, the increasing networking of com- puters and the proliferation of personal computers transformed computer crime and saw the introduction of specific computer crime laws. The evolution of such legislation followed successive waves, reflecting con- cerns surrounding the misuse of computers. Initial concerns which related to unauthorized access to private information expanded into concern that comput- ers could also be used for economic crimes. As computers became more and more centralized, the concern was to protect against unauthorized access to computer data. Increasing connectivity not only magnified these concerns. It gave rise to new problems, such as remote attacks on computers and networks, and gave new life to old offences such as infringement of copyright, distribution of child pornography, and global fraudulent schemes. 2.1 The Challenges of Cybercrime Rapid technological development continues to present new challenges. The in- creasing uptake of broadband allows many home users to leave their computers connected to the Internet, thus making them more vulnerable to external at- tack. Peer-to-peer technology may not only be used to transfer illegal content, but also to orchestrate massive attacks. The convergence of telecommunications and computing has transformed mobile phones into miniature networked com- puters, with attendant potential for criminality. According to Jonothon Clough, the three necessary factors to commit a crime are motivated offenders, opportunity, and the absence of capable guardians. While there are many opportunities for the offenders in the digital world, we summarize the key features of digital technology that facilitates the crime in table 1. Published by SMU Scholar, 2019 3 SMU Data Science Review, Vol. 2 [2019], No. 2, Art. 9 Challenge Description Scale the Internet allows users to communicate with each other. Internet users provide an unprecedented pool of potential offenders and victims. This acts as a \force multiplier", al- lowing offending to be committed on a very large scale. The ability to automate several processes further amplifies this effect. Accessibility The technology has become ubiquitous and increasingly easy to use, ensuring its availability to both offenders
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