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Wireless sensor network intrusion detection method based on deep learning

A wireless sensor network and intrusion detection technology, which is applied in neural learning methods, wireless communication, transmission systems, etc., can solve the problems of low detection accuracy, inability to extract abstract features of sample data, and low detection efficiency, so as to improve intrusion detection. Detection accuracy, solving low detection accuracy and detection efficiency, and improving detection efficiency

Inactive Publication Date: 2018-06-29
CHONGQING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

[0004] In traditional neural network methods, most of them belong to shallow learning networks, which cannot extract the most essential abstract features of sample data, resulting in low detection accuracy and high complexity of algorithm rules, resulting in long training and learning time and low detection accuracy. low efficiency

Method used

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  • Wireless sensor network intrusion detection method based on deep learning
  • Wireless sensor network intrusion detection method based on deep learning
  • Wireless sensor network intrusion detection method based on deep learning

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Embodiment Construction

[0031] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0032] refer to figure 1 The basic structure diagram shown in the deep learning-based wireless sensor network intrusion detection method includes the following steps:

[0033] 101: Data preprocessing: First, perform data standardization and normalization on the latest network intrusion dataset NSL-KDD.

[0034] 102: After data preprocessing, randomly select 10% of the training set as training samples for training and learning, and simultaneously select 10% of the test set as detection samples for subsequent intrusion detection method detection algorithms.

[0035]103: Construct a multi-layer neural network structure and use deep learning methods to extract the most essential abstract features of samples. The multi-layer network structure is a network structure based on deep belief nets (DBN), which is composed of several restricted boltzm...

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Abstract

The invention relates to a wireless sensor network intrusion detection method based on deep learning and belongs to the technical field of safety of a sensor network; according to the method, a multi-layer neural network structure is used and an abstraction feature of the highest level of the data is extracted; feature learning characteristics of a deep belief network and an extreme learning machine are used for rapidly learning the feature and feature extraction of the network data is performed to construct a base classifier; then, the relatively strong classification capability of a random forest algorithm is used for combining a plurality of the base classifiers into a strong classifier; therefore, the method is high in detection accuracy, fast in defection speed and has great detectioncapability for the network intrusion behavior.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor network security, and in particular relates to a deep learning-based wireless sensor network intrusion detection method. Background technique [0002] In recent years, driven by the "Internet +" development strategy and emerging innovative applications of the Internet of Things, the wireless sensor network (Wireless sensor network, WSN) has developed rapidly around the world, and its applications are no longer limited to the military field. Instead, it extends to all aspects of people's lives, such as healthcare, smart home, environmental monitoring, commercial and industrial fields, etc. Wireless sensor networks have become an important research and development field. Wireless sensor networks are generally deployed in harsh environments, uninhabited areas or enemy positions, coupled with the inherent vulnerability of wireless sensor networks such as limited energy and computing resources...

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Application Information

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IPC IPC(8): H04L29/06H04W12/12G06N3/08G06K9/62H04W12/121
CPCH04L63/1416H04W12/12G06N3/08G06F18/241
Inventor 胡向东程占喻白银唐贤伦韩恺敏
Owner CHONGQING UNIV OF POSTS & TELECOMM
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