Detection of advanced persistent threat using machine-learning correlation analysis
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 34
- Type
- D - Journal article
- DOI
-
10.1016/j.future.2018.06.055
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 349
- Volume
- 89
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Technical exception
- Month of publication
- -
- Year of publication
- 2018
- URL
-
https://www.sciencedirect.com/science/article/abs/pii/S0167739X18307532?via%3Dihub
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
-
-
- Citation count
- 39
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work has an immediate impact on advancing the state-of-the-art in using Artificial Intelligence (AI) for Intrusion Detection Systems (IDSs), modelling and producing solutions for the detection of Advanced Persistent Threats (APTs). This paper is significant because it is an outcome of an international collaboration between seven researchers from four leading universities in Europe. Furthermore, due to the lack of relevant publicly available data for APT scenarios, an APT dataset has been generated. Many researchers in the community have already used and benefited from this dataset. Therefore, it is a highly cited paper.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
