Auxiliary Entropy Reduction Based Intrusion Detection Model for Ordinary Differential Equations

doi:10.3969/j.issn.1671-1122.2022.06.001

Abstract

Abstract:

In order to improve the detection efficiency and classification accuracy of the intrusion detection task of deep learning model, this paper proposes an intrusion detection model based on the auxiliary entropy subtraction of the divine ordinary differential equation (E-ODEnet). This intrusion detection model defines continuous hidden states by parametric ordinary differential equations, while does not require further hierarchical propagation of gradients with updated parameters, while reducing memory consumption and greatly improving efficiency. Feature dimensionality reduction is performed using information bottlenecks to extract the main information relevant to the classification task, while label smoothing and entropy reduction loss are used to improve the generalization ability and accuracy of the model. This experiment is trained and tested on the NSL-KDD dataset, and the accuracy rate of the experimental result is 99.76%, which is better than other intrusion detection models.

Key words: intrusion detection, entropy, ODEnet, NSL-KDD

CLC Number:

TP309

ZHANG Xinglan, FU Juanjuan. Auxiliary Entropy Reduction Based Intrusion Detection Model for Ordinary Differential Equations[J]. Netinfo Security, 2022, 22(6): 1-8.

Figures/Tables 11

References 18

[1]	KANG M J, KANG J W, TANG T. Intrusion Detection System Using Deep Neural Network for in-Vehicle Network Security[EB/OL]. [2021-10-28]. https://xueshu.baidu.com/usercenter/paper/show?paperid=345b81696b59762c8cb484998e10a90e.
[2]	LOUKAS G, VUONG T. Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning[J]. IEEE Access, 2017(6): 3491-3508.
[3]	KIM J, SHIN N, JO S Y, et al. Method of Intrusion Detection Using Deep Neural Network[C]// IEEE. IEEE International Conference on Big Data and Smart Computing. New York: IEEE, 2017: 313-316.
[4]	YIN Chuanlong, ZHU Yuefei. A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks[J]. IEEE Access, 2017(5): 21954-21961.
[5]	ZHAO Guangzheng, ZHANG Cuixiao, ZHENG Lijuan. Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network[C]// IEEE. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). New York:IEEE, 2017: 639-642.
[6]	WU Kehe, CHEN Zuge, LI Wei. A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks[J]. IEEE Access, 2018(6): 50850-50859.
[7]	SHONE N, NGOC T N, PHAI V D, et al. A Deep Learning Approach to Network Intrusion Detection[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2(1): 41-50. doi: 10.1109/TETCI.2017.2772792 URL
[8]	KASONGO S M, SUN Yanxia. A Deep Learning Method with Filter Based Feature Engineering for Wireless Intrusion Detection System[J]. IEEE Access, 2019(7): 38597-38607.
[9]	TANG T A, MHAMDI L. Deep Recurrent Neural Network for Intrusion Detectionin SDN-Based Networks[C]// IEEE. IEEE International Conference on Network Softwarization (NetSoft). New York:IEEE, 2018: 202-206.
[10]	SALAMA M A, EID H F, RAMADAN R A, et al. Hybrid Intelligent Intrusion Detection Scheme[C]// Springer. Online World Conference on Soft Computing in Industrial Applications. Berlin: Springer, 2010: 293-303.
[11]	ZHANG Yong, CHEN Xu, JIN Lei, et al. Network Intrusion Detection: Based on Deep Hierarchical Network and Original Flow Data[J]. IEEE Access, 2019( 7): 37004-37016.
[12]	SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-V4, Inception-ResNet and the Impact of Residual Connections on Learning[J]. Computer Vision and Pattern Recognition, 2017: 4278-4284.
[13]	CHEN R T Q. Neural Ordinary Differential Equations[EB/OL]. [2021-10-26]. https://arxiv.org/abs/1806.07366.
[14]	ALEMI A A. Deep Variational Information Bottleneck[EB/OL]. [2021-10-05]. https://arxiv.org/abs/1612.00410.
[15]	TAVALLAEE M, BAGHERI E, LU W, et al. A Detailed Analysis of the KDD CUP 99 Data Set[C]// IEEE. 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. New York: IEEE, 2009: 1-6.
[16]	REVATHI S, MALATHI A. A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection[J]. International Journal of Engineering Research & Technology, 2013, 2(12): 1848-1853.
[17]	DIRO A A, CHILAMKURTI N. Distributed Attack Detection Scheme Using Deep Learning Approach for Internet of Things[J]. Future Generations Computer Systems, 2017, 82(5): 761-768. doi: 10.1016/j.future.2017.08.043 URL
[18]	GU Jie, LU Shan. An Effective Intrusion Detection Approach Using SVM with Nave Bayes Feature Embedding[EB/OL]. [2021-10-28]. https://xueshu.baidu.com/usercenter/paper/show?paperid=11250ev0nc390vc01y1f0250fu672113.

	数据总数	正常数据	Dos	Probe	R2L	U2R
KDDTrain⁺	125973	67343	45927	11656	995	52
KDDTest⁺	22544	9711	7458	2421	2754	200
KDDTest^-21	11850	2152	4342	2402	2754	200

真实值预测值	异常	正常
异常	TN	FP
正常	FN	TP

模型	ACC	P	R	F1
BiLSTM	99.14%	99.26%	99.14%	99.20%
ResNet	99.57%	99.61%	99.53%	99.57%
本文方案	99.76%	99.73%	99.75%	99.74%

方法	数据集	ACC	P	R	F1
DIRO	NSL-KDD	99.20%	99.02%	99.27%	99.14%
GU	NSL-KDD	99.35%	—	99.24%	—
本文方案	NSL-KDD	99.76%	99.73%	99.75%	99.74%

模型	准确率	时间/s
ResNet	99.57%	0.24
多层感知机（MLP）	99.23%	0.023
本文方案	99.76%	0.098