TY  - EJOU
AU  - Wu, Xiaonian  
AU  - Zhang, Chuyun  
AU  - Zhang, Runlian  
AU  - Wang, Yujue  
AU  - Cui, Jinhua  

TI  - A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data
T2  - Computers, Materials \& Continua

PY  - 2018
VL  - 56
IS  - 1
SN  - 1546-2226

AB  - 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.
KW  - Distributed intrusion detection
KW  -  data allocation
KW  -  load balancing
KW  -  data integrity
KW  -  big data

DO  - 10.3970/cmc.2018.02449