Efficient machine learning for attack detection
| dc.contributor.author | Wressnegger, Christian | |
| dc.date.accessioned | 2021-06-21T09:47:16Z | |
| dc.date.available | 2021-06-21T09:47:16Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Detecting and fending off attacks on computer systems is an enduring problem in computer security. In light of a plethora of different threats and the growing automation used by attackers, we are in urgent need of more advanced methods for attack detection. Manually crafting detection rules is by no means feasible at scale, and automatically generated signatures often lack context, such that they fall short in detecting slight variations of known threats. In the thesis “Efficient Machine Learning for Attack Detection” [35], we address the necessity of advanced attack detection. For the effective application of machine learning in this domain, a periodic retraining over time is crucial. We show that with the right data representation, efficient algorithms for mining substring statistics, and implementations based on probabilistic data structures, training the underlying model for establishing an higher degree of automation for defenses can be achieved in linear time. | en |
| dc.identifier.doi | 10.1515/itit-2020-0015 | |
| dc.identifier.pissn | 2196-7032 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36582 | |
| dc.language.iso | en | |
| dc.publisher | De Gruyter | |
| dc.relation.ispartof | it - Information Technology: Vol. 62, No. 5-6 | |
| dc.subject | Language models | |
| dc.subject | classification | |
| dc.subject | anomaly detection | |
| dc.subject | malware detection | |
| dc.title | Efficient machine learning for attack detection | en |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 286 | |
| gi.citation.publisherPlace | Berlin | |
| gi.citation.startPage | 279 |