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

被引：11

作者：

Wu, Xiaonian ^{[1
]}

Zhang, Chuyun ^{[3
]}

Zhang, Runlian ^{[2
]}

Wang, Yujue ^{[2
]}

Cui, Jinhua ^{[4
]}

机构：

[1] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China

[2] Guilin Univ Elect Technol, Guangxi Key Lab Cryptog & Informat Secur, Guilin 541004, Peoples R China

[3] Guilin Univ Elect Technol, Guangxi Wireless Broadband Commun & Signal Proc K, Guilin 541004, Peoples R China

[4] Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore

来源：

CMC-COMPUTERS MATERIALS & CONTINUA | 2018年 / 56卷 / 01期

关键词：

Distributed intrusion detection; data allocation; load balancing; data integrity; big data; HIGH-SPEED NETWORKS;

D O I：

10.3970/cmc.2018.02449

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：61 / 72

页数：12

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