Analysis of malware prediction based on infection rate using machine learning techniques
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In this modern, technological age, the internet has been adopted by the masses. And with it, the danger of malicious attacks by cybercriminals have increased. These attacks are done via Malware, and have resulted in billions of dollars of financial damage. Which is why prevention of malware attacks has become an essential part of the battle against cybercrime. In recent years, Machine Learning has become an important tool in the field of Malware Detection, which is the first step towards removing malware from infected devices. In this thesis, we are applying machine learning algorithms to predict the malware infection rates of computers based on its features. We are using supervised machine learning algorithms and gradient boosting algorithms, such as LightGBM, Neural Networks, and Decision Tree Learning. We have collected a publicly available dataset, which was divided into two parts, one being the training set, and the other will be the testing set. After conducting four different experiments using the aforementioned algorithms, it has been discovered that LightGBM is the best model with an AUC Score of 0.73926.