Airborne network intrusion detection method based on deep learning

An airborne network and intrusion detection technology, applied in the field of network security, can solve the problems of poor adaptive ability, high false negative rate, dimension explosion, etc., and achieve the effect of reducing the probability of being attacked and improving the detection performance.

Inactive Publication Date: 2019-04-16
CIVIL AVIATION UNIV OF CHINA
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Problems solved by technology

[0004] However, as network intrusion data is becoming more and more complex and feature-diversified, and limited by the constraints of time and space complexity, traditional machine learning algorithms often perform poorly and are prone to "dimension explosion". It leads to high false positive rate, high false negative rate and poor adaptive ability of the detection model

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  • Airborne network intrusion detection method based on deep learning
  • Airborne network intrusion detection method based on deep learning

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but the following embodiments in no way limit the present invention.

[0025] Such as figure 1 As shown, the airborne network intrusion detection method based on deep learning provided by the present invention includes the following steps carried out in order:

[0026] 1) Monitor and collect the communication data packets exchanged by the airborne network, and add a time stamp;

[0027] A network card set to promiscuous mode is connected to the Passenger Information and Entertainment Service Domain (PIESD), and the data flow passing through the network card is monitored and collected.

[0028] 2) Carry out feature mapping to the communication data packet obtained in step 1), extract network features including protocol type, service type, connection state, and generate feature data sets by these network features;

[0029] 3) To preprocess the ab...

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Abstract

The invention discloses an airborne network intrusion detection method based on deep learning. The method includes the steps of data packet monitoring, data packet collection, time stamp labeling, filtering sampling, feature mapping, character-type feature digitization, normalization processing, feature learning, performance evaluation and the like. Compared with traditional detection models, an airborne network intrusion detection model generated by the method provided by the invention has better detection performance, and decreases probability that an airborne network is attacked due to missed detection or false reporting.

Description

technical field [0001] The invention belongs to the technical field of network security, in particular to an airborne network intrusion detection method based on deep learning. Background technique [0002] Airborne networking has achieved rapid development worldwide and is expected to create a market worth $130 billion in the next 20 years. But the increased openness of an aircraft once considered the last cyber island has also opened the door to sophisticated and varied cyberattacks. The security issues brought about by openness pose challenges to the airborne network in the rapid development stage, and the research on airborne network security issues is imminent. [0003] Based on the concept of multiple independent security layers (MILS), Jacob proposed intermediate services such as labeling, filtering, and information flow control, and used a partitioned communication system to be responsible for the communication between MILS nodes. Laarouchi et al. proposed a soluti...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06G06N3/04
CPCH04L63/1416G06N3/045
Inventor 杨宏宇叶里谢丽霞
Owner CIVIL AVIATION UNIV OF CHINA
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