D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 4868507
- Type
- D - Journal article
- DOI
-
10.1016/j.measurement.2014.04.034
- Title of journal
- Measurement
- Article number
- -
- First page
- 212
- Volume
- 55
- Issue
- -
- ISSN
- 0263-2241
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2014
- URL
-
-
- 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
-
5
- Research group(s)
-
-
- Citation count
- 67
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The resource-limited nature of wireless sensor network nodes renders them potentially vulnerable to attack. This paper introduces a hybrid clustering method and its successful application to enhance anomaly-based detection of malicious behaviour (evaluated in the context of effectively combatting distributed attacks against wireless sensor networks). The work has been significantly influential in both informing subsequent research targeting intrusion detection and defence against network-based attacks, alongside further works reflecting wider reference to the hybrid clustering approach. As such, it demonstrates cross-disciplinary impact, receiving significant attention beyond the computer science domain.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
