A phishing detection method based on salps swarm algorithm support vector machine

Abstract

The invention discloses a phishing detection method based on a salps swarm algorithm support vector machine. First, the basic parameters of the salps swarm algorithm are initialized: the population number, the number of iterations, the individual dimension, and the search space; the position and range of the individual are randomly initialized; Then it is divided into leader salps and follower salps according to the size of the fitness value, and the optimal parameters of the support vector machine are excavated by the coordination and cooperation of these two salps. In each iteration, the function used to evaluate the fitness value of the individual is the detection accuracy of the parameters carried by the individual on the phishing website dataset. The present invention and general optimization algorithms such as genetic algorithm, gravitational search algorithm, bat algorithm, particle swarm algorithm, etc., can dig out the optimal parameter parameters of the support vector machine as much as possible on the optimization support vector machine, and improve the performance of the support vector machine in fishing. detection accuracy.

Description

technical field [0001] The invention belongs to the technical fields of intelligent optimization, machine learning and information security, and relates to a phishing detection method, in particular to a phishing detection method based on a salps swarm algorithm support vector machine. Background technique [0002] Phishing is a malicious website designed to deceive users' personal information by imitating real web pages. With the development of the Internet, more and more people conduct transactions or register online, and at the same time store their account passwords or input their personal information online. Then criminals create malicious web pages that are very similar to real web pages to deceive users' information. According to statistics, as of 2015, the number of phishing websites has reached more than 260,000, and the number has continued to increase significantly, which has caused great hidden dangers to users' information security. [0003] With the rise of m...

AI Technical Summary

Problems solved by technology

Traditional grid search, particle swarm algorithm, genetic algorithm, etc. all try to optimize the parameters of the support vector machine, but it is easy to fall into a local optimum, and it is difficult to make the support vector machine take full advantage

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