Design of network security monitoring system based on CNN and exponential weighted D-S evidence theory

ABSTRACT

To address the poor network security protection and low attack traffic identification, the study designs a network security monitoring system based on convolutional neural network and exponential weighted Dempster Shafer evidence theory. The validation of the UNSW-NB15 data set showed that the output of multi-source fusion after exponential weighted Dempster Shafer evidence theory was higher than the output of the feature fusion by 3.92%. The accuracy of the attack recognition was as high as 93.72%, which was 1.85% higher than feature fusion. The accuracy of the proposed network security monitoring system increased by 3.70% on average over other methods. The results indicate that the proposed network security monitoring system can effectively improve the efficiency of network attack identification, monitor network security in real-time, and effectively protect network operation. The system is feasible and reasonable in terms of network security situational awareness, which can provide effective situational analysis for network administrators.

KEYWORDS:

• Convolutional neural network
• multi-source fusion
• network security
• traffic monitoring
• exponential weighted D-S evidence theory

Notations

Abbreviations	=	Full name
NSSAT	=	Network Security Situational Awareness Technology
CNN	=	Convolutional Neural Networks
CK	=	Convolutional Kernel
D-S	=	Dempster-Shafer
EWD-S	=	Exponential Weighted D-S
IoT	=	Internet of Things
ReLU	=	Rectified Linear Unit
IP	=	Internet Protocol
ROC	=	Receiver Operating Characteristic curve
FPR	=	False Positive Rate
FNR	=	False Negative Rate
AUC	=	Area Under the Curve
FAR	=	False Alarm Rate
DNS	=	Domain Name System
HTTP	=	Hypertext Transfer Protocol
SMTP	=	Simple Mail Transfer Protocol

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used to support the findings of the research are available from the corresponding author upon reasonable request.

Additional information

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.