Deep abstraction and weighted feature selection for Wi-Fi impersonation detection
- Submitting institution
-
Birkbeck College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 203
- Type
- D - Journal article
- DOI
-
10.1109/TIFS.2017.2762828
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 621
- Volume
- 13
- Issue
- 3
- ISSN
- 1556-6013
- Open access status
- Technical exception
- Month of publication
- October
- Year of publication
- 2017
- URL
-
http://eprints.bbk.ac.uk/id/eprint/24453/
- 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
-
4
- Research group(s)
-
2 - Experimental Data Science
- Citation count
- 46
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is one of the flagship papers in the area of IoT security, using a data-driven approach for the detection of impersonation attacks which has been identified as one of the most significant cyber-attacks in connected systems. Leading cyber-security research centres (inc. Security Lab, Samsung SDS) compared their algorithms against the performance of the system proposed in this paper, leading to the award of a research grant of $100,000 from Samsung’s GRO Program.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -