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A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data

Xiaonian Wu1,*, Chuyun Zhang3, Runlian Zhang2, Yujue Wang2, Jinhua Cui4

Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China.
Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory, Guilin University of Electronic Technology, Guilin 541004, China.
School of Information Systems, Singapore Management University, Singapore 178902, Singapore.

* Corresponding Author: Xiaonian Wu. Email: .

Computers, Materials & Continua 2018, 56(1), 61-72. 10.3970/cmc.2018.02449


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.


Cite This Article

X. . Wu, C. . Zhang, R. . Zhang, Y. . Wang and J. . Cui, "A distributed intrusion detection model via nondestructive partitioning and balanced allocation for big data," Computers, Materials & Continua, vol. 56, no.1, pp. 61–72, 2018.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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