Feature analysis on the containment time of an incident
Başlık çevirisi mevcut değil.
- Tez No: 786648
- Danışmanlar: DR. BENJAMİN AZIZ
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Yönetim Bilişim Sistemleri, Computer Engineering and Computer Science and Control, Management Information Systems
- Anahtar Kelimeler: Data driven security management, Feature analysis, Organisational dataset, WEKA, Information Security
- Yıl: 2017
- Dil: İngilizce
- Üniversite: Unıversıty Of Portsmouth
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
Özet yok.
Özet (Çeviri)
Data is the most important point for researchers however if the raw data is not used to uncover the hidden patterns, it does not make sense or it can be just data forever. One way to uncover hidden patterns on the big dataset is data driven security management systems, they have been widely used as the common goal to deduce hidden patterns on the big dataset. Companies has been pushed to transform themselves inside data-driven organizations because of the technological development in the big data infrastructure. It causes to share dataset on the internet among the companies for people who are interested in data/ dataset. This research aims to find out relevant features on the containment time of an incident for predicting containment time of it in a big organisational dataset. In this report, the Veris Community Database (VCDB) open dataset is used, the WEKA data-mining tool is used to achieve the aim of this paper. The research`s requirements are gathered through reviewing related work and attribute selection approaches are proposed attempting to solve the problems by investigating existing approaches and machine learning algorithms. In this approach, it attempts to use attribute selection filters on the WEKA tool, the containment time is given to tool as the class value. More than two hundred features over two thousand and around six thousand eight hundred incidents are used to obtain the relevant features on the containment time. Thirteen relevant features were found on containment time of an incident. Relevant features were from; action, asset, victim, attribute, and timeline properties of an incident. These features were discussed in terms of the organizations` information security. Finally, future work is declaimed for improving the approach in terms of those weaknesses.
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