Deep Learning Approach for Intelligent Intrusion Detection System
- Submitting institution
-
University of East London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2019.2895334
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 41525
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Exception within 3 months of publication
- Month of publication
- -
- Year of publication
- 2019
- 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)
-
3 - Secure Software Engineering
- Citation count
- 105
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- To protect systems at network & host levels against cyber-attacks we propose a hybrid intrusion detection alert system using distributed deep learning models with DNNs for handling and analysing various large data sets. This can be used in real time to effectively monitor network traffic and host-level events to proactively alert the administrator to possible cyber-attacks. The results of this work led to Postdoctoral Research in 2019/20, where it was used in developing and implementing novel computational and machine learning algorithms and applications for big data integration and data mining with Cincinnati Children's Hospital Medical Centre, Cincinnati, USA.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -