| Title | Cryptojacking Detection with CPU Usage Metrics |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Gomes, F., Correia, M. |
| Conference Name | 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA) |
| Date Published | nov |
| Keywords | Central Processing Unit, cryptography, Human Behavior, machine learning, Malware, Measurement, Metrics, Monitoring, pubcrawl, resilience, Resiliency, Web pages |
| Abstract | Cryptojacking is currently being exploited by cyber-criminals. This form of malware runs in the computers of victims without their consent. It often infects browsers and does CPU-intensive computations to mine cryptocurrencies on behalf of the cyber-criminal, which takes the profits without paying for the resources consumed. Such attacks degrade computer performance and potentially reduce the hardware lifetime. We introduce a new cryptojacking detection mechanism based on monitoring the CPU usage of the visited web pages. This may look like an unreliable way to detect mining malware since many web sites are heavy computationally and that malware often throttles CPU usage. However, by combining a set of CPU monitoring features and using machine learning, we manage to obtain metrics like precision and recall close to 1. |
| DOI | 10.1109/NCA51143.2020.9306696 |
| Citation Key | gomes_cryptojacking_2020 |
Cryptojacking Detection with CPU Usage Metrics