A machine learning framework for malware triage
| dc.contributor.author | Danaeifard, Soroush | |
| dc.contributor.supervisor | Traore, Issa | |
| dc.contributor.supervisor | Woungang, Isaac | |
| dc.date.accessioned | 2024-08-14T19:15:28Z | |
| dc.date.available | 2024-08-14T19:15:28Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Engineering MEng | |
| dc.description.abstract | Every day, thousands of new malicious software emerge globally, posing threats to consumer devices, stealing private data, or inducing financial losses. The increasing number and sophistication of malware threats underscores the need for effective and efficient malware detection and triage schemes. Malware triage is a process used by cybersecurity professionals to quickly assess, prioritize, and respond to malware incidents. Effective malware triage requires a combination of automated tools, skilled personnel, and well-defined procedures to quickly and accurately respond to malware incidents, minimizing damage and recovery time. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/18521 | |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | machine learning | |
| dc.title | A machine learning framework for malware triage | |
| dc.type | project |