| Title | Analysis of Malware Prediction Based on Infection Rate Using Machine Learning Techniques |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | bin Asad, Ashub, Mansur, Raiyan, Zawad, Safir, Evan, Nahian, Hossain, Muhammad Iqbal |
| Conference Name | 2020 IEEE Region 10 Symposium (TENSYMP) |
| Keywords | Decision Tree, Decision trees, Human Behavior, k-fold, lgbm, machine learning algorithm, machine learning algorithms, Malware, malware analysis, malware prediction, Microsoft malware dataset, Neural Network, Neural networks, Prediction algorithms, Predictive Metrics, Predictive models, privacy, pubcrawl, Resiliency, Training |
| Abstract | 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. This makes the prevention of malicious attacks an essential part of the battle against cybercrime. In this paper, 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. 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. |
| DOI | 10.1109/TENSYMP50017.2020.9230624 |
| Citation Key | bin_asad_analysis_2020 |
Analysis of Malware Prediction Based on Infection Rate Using Machine Learning Techniques