| Title | Taking advantage of unsupervised learning in incident response |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Nilă, Constantin, Patriciu, Victor |
| Conference Name | 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) |
| Date Published | jun |
| Keywords | Automated Response Actions, composability, computer security, cybersecurity, data mining, dimensionality reduction, feature extraction, machine learning, Malware, pubcrawl, quick incident response, Resiliency, Tools, Training |
| Abstract | This paper looks at new ways to improve the necessary time for incident response triage operations. By employing unsupervised K-means, enhanced by both manual and automated feature extraction techniques, the incident response team can quickly and decisively extrapolate malicious web requests that concluded to the investigated exploitation. More precisely, we evaluated the benefits of different visualization enhancing methods that can improve feature selection and other dimensionality reduction techniques. Furthermore, early tests of the gross framework have shown that the necessary time for triage is diminished, more so if a hybrid multi-model is employed. Our case study revolved around the need for unsupervised classification of unknown web access logs. However, the demonstrated principals may be considered for other applications of machine learning in the cybersecurity domain. |
| DOI | 10.1109/ECAI50035.2020.9223163 |
| Citation Key | nila_taking_2020 |
Taking advantage of unsupervised learning in incident response