Feature analysis on the containment time for cyber security incidents
Yazarlar (1)
Dr. Öğr. Üyesi Gülsüm AKKUZU KAYA
University Of Portsmouth, İngiltere
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/ICWAPR.2018.8521252 | ||
| Kongre Adı | 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) | ||
| Kongre Tarihi | 15-07-2018 / | ||
| Basıldığı Ülke | İngiltere | Basıldığı Şehir | |
| Bildiri Linki | https://ieeexplore.ieee.org/abstract/document/8521252 | ||
| Özet |
| Data mining techniques have been widely used as a common goal to discover hidden patterns from big data sets, so researchers have been motivated to make use of data in discovering useful information. The main contribution of this paper lies in its identifying relevant features from an open data set to predict the containment time of Cyber incidents. In particular, 13 relevant features were identified and selected to come up with a predictive model. Our results are discussed in the context of the organization's' information security. |
| Anahtar Kelimeler |
| Data-driven security management | Feature selection | Information security | Machine learning | Organizational data set | WEKA tool |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| SCOPUS | 2 |