Hidden Markov Models and Alert Correlations for the Prediction of Advanced Persistent Threats
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 36
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2019.2930200
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 99508
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Technical exception
- Month of publication
- -
- Year of publication
- 2019
- URL
-
https://ieeexplore.ieee.org/document/8767917
- 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
- 9
- 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 Intrusion Detection Systems (IDSs), modelling and producing solutions for the detection of Advanced Persistent Threats (APTs). This paper is significant because it is one of the outcomes of an international collaboration between two research teams in two leading universities in the UK and Asia. Furthermore, due to the lack of relevant publicly available data for APT scenarios, an APT dataset has been generated and made publicly available. Thus, many researchers in the community have already used and benefited from this dataset.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
