Abstract
Hybrid Anomaly Detection Model (HADM) is a security platform to detect and prevent cyber-attacks on communication networks. The platform uses a combination of linear and learning algorithms combined with protocol analyzer. The linear algorithms filter and extract distinctive attributes and features of the cyber-attacks while the learning algorithms use these attributes and features to identify new types of cyber-attacks. The protocol analyzer in this platform classifies and filters vulnerable protocols to avoid unnecessary computation load. The use of linear algorithms in conjunction with learning algorithms allows the HADM to achieve improved efficiency in terms of accuracy and computation time in order to detect cyber-attacks over existing solutions.
Original language | English |
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Title of host publication | Wired/Wireless Internet Communications - 16th IFIP WG 6.2 International Conference, WWIC 2018, Proceedings |
Editors | Kaushik Roy Chowdhury, Marco Di Felice, Bo Sheng, Ibrahim Matta |
Publisher | SPRINGER |
Chapter | 2 |
Pages | 107-118 |
Number of pages | 12 |
ISBN (Electronic) | 978-3-030-02931-9 |
ISBN (Print) | 978-3-030-02930-2 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
MoE publication type | A4 Article in a conference publication |
Event | International Conference on Wired/Wireless Internet Communications - Boston, United States Duration: 18 Jun 2018 → 20 Jun 2018 Conference number: 16 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10866 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Wired/Wireless Internet Communications |
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Abbreviated title | WWIC |
Country/Territory | United States |
City | Boston |
Period | 18/06/2018 → 20/06/2018 |
Keywords
- Anomaly detection
- Cloud computing
- Internet of things
- Machine learning
- Security