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Intrusion detection method for optimizing regularization extreme learning machine through improved longicorn swarm algorithm

An extreme learning machine and intrusion detection technology, which is applied in neural learning methods, calculations, calculation models, etc., can solve problems such as intrusion detection capabilities of algorithms without excellent classification performance, improve detection accuracy and training speed, reduce complexity, and improve The effect of convergence speed

Active Publication Date: 2020-07-14
JIANGXI UNIV OF SCI & TECH
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Problems solved by technology

[0004] For this reason, the embodiment of the present invention provides an intrusion detection method for optimizing the regularized extreme learning machine by improving the beetle swarm algorithm, so as to solve the problem that the algorithm in the prior art does not have excellent classification performance and good intrusion detection ability

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  • Intrusion detection method for optimizing regularization extreme learning machine through improved longicorn swarm algorithm
  • Intrusion detection method for optimizing regularization extreme learning machine through improved longicorn swarm algorithm
  • Intrusion detection method for optimizing regularization extreme learning machine through improved longicorn swarm algorithm

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Embodiment

[0032] Embodiment: a kind of intrusion detection method that improves the Tianniu herd algorithm to optimize the regularized extreme learning machine, such as figure 1 shown, specifically the following steps

[0033] S1: Initialize the parameters of the regularized extreme learning machine model, obtain the input layer nodes, hidden layer nodes and output layer nodes, as well as the initial weight and threshold of the network;

[0034] S2: Obtain the global optimal position by using the improved swarm algorithm, and the optimal position is the optimal initial weight and threshold. Among them, the improved Tianniu algorithm specifically includes the following steps

[0035] S101: Use the Tent mapping reverse learning method to initialize the beetle group, and obtain the input layer nodes, hidden layer nodes and output layer nodes as well as network initial weights and thresholds;

[0036] S102: Calculate the fitness function value of the beetle group according to the training...

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Abstract

The embodiment of the invention discloses an intrusion detection method for optimizing a regularization extreme learning machine through an improved longicorn swarm algorithm, and the method introduces an LU decomposition method to solve an output weight through iteration, reduces the calculation complexity, and improves the intrusion detection accuracy. The improved longicorn swarm algorithm is introduced for RELM neural network parameter optimization so as to improve the detection precision and the training speed of the RELM neural network; according to the improved longicorn swarm algorithm, Tent mapping reverse learning is used to initialize a population, a Levy flight population strategy and a dynamic mutation strategy, so that individuals dynamically learn the experience of the population in the moving process, the convergence rate of the algorithm is improved, the later exploration capability is enhanced, and the algorithm is prevented from falling into local optimum.

Description

technical field [0001] The embodiment of the present invention relates to the technical field of intrusion detection methods, in particular to an intrusion detection method for optimizing regularized extreme learning machines by improving the beetle herd algorithm. Background technique [0002] With the rapid development of network technology, the network structure is becoming more and more complex, and the risk of network intrusion is also increasing. How to identify various network intrusions has become a matter of great concern to people. Intrusion Detection (ID) technology is a new type of security mechanism that can dynamically monitor, prevent and defend against intrusions, and has gradually developed into a key technology to ensure network system security. The core of this technology is to detect whether various behaviors in the network are safe by analyzing collected network data. Anomaly detection and misuse detection are two different detection types employed by i...

Claims

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Application Information

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IPC IPC(8): H04L29/06G06N3/00G06N3/04G06N3/08
CPCH04L63/1441G06N3/006G06N3/08G06N3/045
Inventor 王振东
Owner JIANGXI UNIV OF SCI & TECH
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