CFA algorithm and BP neural network-based invasion detection method

A BP neural network and intrusion detection technology, applied in the field of network intrusion detection technology research, can solve problems such as low detection accuracy, slow detection speed, and slow convergence speed

Active Publication Date: 2017-10-24
GUANGDONG UNIV OF TECH
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Problems solved by technology

[0009] The present invention aims to improve the traditional BP neural network in network intrusion detection due to the shortcom

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  • CFA algorithm and BP neural network-based invasion detection method
  • CFA algorithm and BP neural network-based invasion detection method
  • CFA algorithm and BP neural network-based invasion detection method

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[0023] The present invention will be further described in detail below with reference to the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0024] An intrusion detection method based on CFA (Cuttle Fish Algorithm) algorithm and BP neural network, specifically including the following steps:

[0025] 1. See figure 1 The operating parameters of the BP neural network mainly include the connection weight and threshold of each neuron. The object optimized by the CFA algorithm is the connection weight and threshold in the BP neural network. The global error function is:

[0026]

[0027] Among them, the input layer has n neurons, the hidden layer has q neurons, and the output layer has m neurons. The total number of samples is P, x pi Represents the i-th input of the p-th sample, v ki Represents the weight from the i-th node in the input layer to the k-th node in the hidden layer, w jk Represents the weight from the kth node in the hidden l...

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Abstract

The invention discloses a CFA algorithm and BP neural network-based invasion detection method. The method comprises the following steps of: encoding operation parameters of a BP neural network as cell individuals in a CFA algorithm; taking an error function as an adaptive value function of the CFA algorithm; after carrying out iteration for multiple times, selecting the parameter with the optimum fitness as an initial weight value and threshold value of the BP neural network to carry out training; and finally applied the trained BP neural network into a classifier for invasion detection. According to the method, the characteristics of global search and high convergence speed of the CFA algorithm, the initial operation parameters of the BP neural network are optimized, and then the classifier which can be applied to network invasion detection is constructed. Through improving the disadvantages of local minimum and low convergence speed, caused by initial parameter randomization, of the BP neural network, the detection correctness of the BP neural network in the network invasion detection is improved.

Description

technical field [0001] The invention relates to the research field of network intrusion detection technology, in particular to an intrusion detection method based on a CFA (Cuttle Fish Algorithm) algorithm and a BP neural network. Background technique [0002] In view of the increasingly severe network security problems, passive defense technologies such as network firewalls and user authentication systems cannot completely solve network and information security problems. [0003] As an active defense technology, intrusion detection technology can continuously monitor computers and network behaviors in the network, not only monitor malicious behaviors from outside the network, but also detect unauthorized and malicious behaviors inside the network system. Intrusion detection technology collects network logs through certain technical means, listens to network communications, and analyzes these logs and data packets to detect whether there are malicious behaviors in the networ...

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Application Information

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IPC IPC(8): G06F21/55G06N3/08
CPCG06F21/55G06N3/084
Inventor 凌捷黄盛罗玉谢锐龚怡
Owner GUANGDONG UNIV OF TECH
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