可变融合的随机注意力胶囊网络入侵检测模型

doi:10.11959/j.issn.1000-436x.2020220

Abstract

Abstract:

In order to enhance the accuracy and generalization of the detection model,an intrusion detection model of random attention capsule network with variable fusion was proposed.Through dynamic feature fusion,the model could better capture data features.At the same time,random attention mechanism was used to reduce the dependence on training data and make the model more generalization.The model was validated on NSL-KDD and UNSW-NB15 datasets.The experimental results show that the accuracy of the model on the two test sets is 99.49% and 98.60% respectively.

Key words: deep learning, intrusion detection, cyberspace security, capsule network, random attention

CLC Number:

TP309

Xinglan ZHANG,Shenglin YIN. Intrusion detection model of random attention capsule network based on variable fusion[J]. Journal on Communications, 2020, 41(11): 160-168.

Figures/Tables 15

References 17

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数据集	Normal/条	DoS/条	Probe/条	U2R/条	R2L/条	Total/条
KDDTrain+	67 345	45 926	11 655	52	995	125 973
KDDTest+	9 711	7 458	2421	200	2 754	22 544

数据集	Normal/条	Fuzzers/条	Analysis/条	Backdoors/条	DoS/条	Exploits/条	Generic/条	Reconnaissance/条	Shellcode/条	Worms/条	Total/条
训练集	56 000	18 184	2 000	1 746	12 264	33 393	40 000	10 491	1 133	130	175 341
测试集	37 000	6 062	677	583	4 089	11 132	18 871	3 496	378	44	82 332

模型	验证集				测试集
模型	AC	P	R	F1-score	AC	P	R	F1-score
KPCA + SVM	98.96%	99.19%	99.22%	99.20%	95.89%	96.23%	96.06%	96.14%
KPCA + KNN	99.33%	99.34%	99.36%	99.35%	97.81%	97.38%	97.89%	97.63%
GBT	99.40%	99.46%	99.47%	99.47%	99.29%	99.23%	99.25%	99.24%
CNN	97.80%	97.82%	97.82%	97.82%	94.69%	94.70%	94.72%	94.71%
CNN+ LSTM	97.88%	97.99%	98.03%	98.01%	95.01%	95.98%	95.99%	95.98%
Vanilla Capsule	99.18%	98.89%	99.19%	99.04%	97.29%	97.30%	97.31%	97.30%
本文模型	99.80%	99.82%	99.82%	99.82%	99.49%	99.47%	99.46%	99.46%

模型	验证集	测试集
Capsule+Self-Attention	99.31%	97.13%
本文模型	99.80%	99.49%

模型	验证集				测试集
模型	AC	P	R	F1-score	AC	P	R	F1-score
KPCA + SVM	98.42%	90.32%	96.33%	93.23%	95.37%	94.99%	95.02%	95.00%
KPCA + KNN	98.79%	98.77%	98.79%	98.78%	96.22%	96.19%	96.17%	96.18%
GBT	99.21%	99.11%	99.10%	99.10%	97.99%	97.98%	97.79%	97.88%
CNN	96.89%	96.90%	96.89%	96.89%	94.20%	94.31%	94.33%	94.32%
CNN + LSTM	97.73%	97.70%	97.71%	97.70%	95.65%	95.34%	95.34%	95.34%
Vanilla Capsule	97.89%	97.83%	97.86%	97.84%	97.47%	97.43%	97.45%	97.44%
本文模型	99.24%	99.21%	99.19%	99.20%	98.60%	98.59%	98.61%	98.60%

Intrusion detection model of random attention capsule network based on variable fusion

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