A Cryptojacking Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent

• Main Author: Kong, Jun Hao
• Format: Undergraduates Project Papers
• Language: English
• Published: 2023
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://umpir.ump.edu.my/id/eprint/40591/1/CB19109.pdf; http://umpir.ump.edu.my/id/eprint/40591/

Cryptocurrency, often known as electronic money, is a currency that exists in digital form. As a result, numerous attackers or hackers are taking advantage of this chance to employ cryptojacking to gain access to a victim's computer or other device resources and mine cryptocurrency without the users' permission. Because the number of cryptojacking attacks is on the rise, this project use machine learning to detect cryptojacking. However, the feature of the data is too many, lowering the machine-learning detection prediction. Hence, a feature selection method is necessary to pick the right features. Aside from that, the objective of this project is to investigate cryptojacking in cryptocurrency users' devices, develop a machine learning model to detect cryptojacking, and evaluate the machine learning model's accuracy, true positive rate (TPR), false positive rate (FPR), and precision in detecting cryptojacking. This project research will present the PMCC Heatmap to choose the optimal attributes for using machine learning to detect cryptojacking in order to utilise machine learning to detect embedded malware. Furthermore, a random forest model is used in this study's machine learning classification. At the end of the process, the system will utilise this model to detect cryptojacking and users will be able to detect new cryptojacking malware based on the model. This study aims to develop a cryptojacking detection system based to the random forest algorithm.