Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2018
Abstract
There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as good load balance, higher detection rate and detection efficiency.
Keywords
Distributed intrusion detection, data allocation, load balancing, data integrity, big data
Discipline
Data Storage Systems
Publication
Computers, Materials & Continua
Volume
56
Issue
1
First Page
61
Last Page
72
ISSN
1546-2218
Identifier
10.3970/cmc.2018.02449
Publisher
Tech Science Press
Citation
WU, Xiaonian; ZHANG, Chuyun; ZHANG, Runlian; WANG, Yujue; and CUI, Jinhua.
A distributed intrusion detection model via nondestructive partitioning and balanced allocation for big data. (2018). Computers, Materials & Continua. 56, (1), 61-72.
Available at: https://ink.library.smu.edu.sg/sis_research/10198
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3970/cmc.2018.02449