Research on Intrusion Detection Mechanism Optimization Based on Federated Learning Aggregation Algorithm under Consortium Chain

doi:10.3969/j.issn.1671-1122.2023.08.007

Abstract

Abstract:

In recent years, federated learning has received widespread attention because it can train and aggregate intrusion detection models while protecting user privacy. As an efficient and controllable distributed ledger technology, consortium blockchain is combined with federated learning technology and applied to multi-node intrusion detection scenarios. However, the algorithm for aggregating intrusion detection models based on federated learning under traditional consortium chains has defects, such as the inability to dynamically adjust aggregation algorithm parameters based on network environment, resulting in high communication costs. Therefore, this paper designed an adaptive federated learning aggregation algorithm based on the consortium chain network environment, dynamically adjusting the intrusion detection model aggregation interval, which balancing the model accuracy and communication cost. Theoretical analysis and experimental results show that, compared with traditional federated learning aggregation algorithms, the intrusion detection model aggregation process in this study reduces the system communication cost while ensuring model accuracy, improves model aggregation efficiency, and has good application prospects.

Key words: consortium blockchain, intrusion detection system, federated learning

CLC Number:

TP309

PENG Hanzhong, ZHANG Zhujun, YAN Liyue, HU Chenglin. Research on Intrusion Detection Mechanism Optimization Based on Federated Learning Aggregation Algorithm under Consortium Chain[J]. Netinfo Security, 2023, 23(8): 76-85.

Figures/Tables 7

References 30

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硬件	型号
CPU	Intel Xeon ProcessorE7-4807
处理器数量	6
内存	32 GB
网络适配器	以太网
硬盘	1 TB

软件	版本号
Ubuntu	20.04 LTS (64 bit)
Hyperledger Fabric	2.2.9
Go	1.19.1
Python	3.9.16

算法	准确率	精确率	召回率	聚合次数
文献[30]算法	65.46%	66.28%	66.46%	415
文献[29]算法	77.86%	72.15%	73.74%	1510
文献[28]算法	90.42%	90.25%	90.42%	5363
本文算法	91.63%	91.35%	91.75%	2170