Malware Classification using Locality Sensitive Hashing and Neural Networks
| dc.contributor.author | Friborg, Ludwig | |
| dc.contributor.author | Peiser, Stefan Carl | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.examiner | HORKOFF, JENNIFER | |
| dc.contributor.supervisor | SCANDARIATO, RICCARDO | |
| dc.date.accessioned | 2019-08-21T12:51:14Z | |
| dc.date.available | 2019-08-21T12:51:14Z | |
| dc.date.issued | 2019 | sv |
| dc.date.submitted | 2019 | |
| dc.description.abstract | In this thesis, we explore the idea of using locality sensitive hashes as input features to a feedforward neural network to perform static analysis to detect JavaScript malware. An experiment is conducted using a dataset containing 1.5M evenly distributed benign and malicious samples provided by the anti-malware company Cyren, which is the industry collaborator for this thesis. Four different locality sensitive hashing algorithms are tested and evaluated: Nilsimsa, ssdeep, TLSH, and SDHASH. The results show a high prediction accuracy of 98.05% and low false positive and negative rates of 0.94% and 2.69% for the best performing models. These results show that LSH based neural networks are a competitive option against other state-of-the-art JavaScript malware classification solutions. | sv |
| dc.identifier.coursecode | DATX05 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/300149 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | locality sensitive hashing | sv |
| dc.subject | static analysis | sv |
| dc.subject | malware detection | sv |
| dc.subject | artificial neural networks | sv |
| dc.subject | machine learning | sv |
| dc.subject | feature extraction | sv |
| dc.title | Malware Classification using Locality Sensitive Hashing and Neural Networks | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H |