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Cyber fraud detection using evolving spiking neural network

机译:使用进化尖峰神经网络进行网络欺诈检测

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摘要

With the rapid growth of the internet, most of the businesses are now moving online. Since the internet is ubiquitous and can be accessed from anywhere websites are susceptible to attacks. One of such attack is phishing website attack. In which an attacker creates a duplicate copy of the website and tries to pose it as a legitimate to steal user's information. So it is the utmost need to detect such phishing websites. Machine learning techniques have been successfully applied to detect the phishing websites. The neural network is one of the efficient ways for detecting these phishing attacks. In our work, we have applied the spiking neural network approach to detect these phishing websites. The spiking neural network is biologically inspired by neuroscience literature, evolving spiking neural classifier for the pattern classification problem. We have compared it with various other machine learning techniques and we show that the evolving spiking neural network performs better than the existing machine learning techniques.
机译:随着Internet的快速发展,大多数企业现在都在线上发展。由于互联网无处不在,并且可以从任何容易受到攻击的网站访问。其中一种攻击是网络钓鱼网站攻击。攻击者在其中创建网站的副本,并试图将其伪装成窃取用户信息的合法网站。因此,最需要检测此类网络钓鱼网站。机器学习技术已成功应用于检测网络钓鱼网站。神经网络是检测这些网络钓鱼攻击的有效方法之一。在我们的工作中,我们应用了尖峰神经网络方法来检测这些网络钓鱼网站。尖峰神经网络受到神经科学文献的生物学启发,针对模式分类问题发展了尖峰神经分类器。我们将其与其他各种机器学习技术进行了比较,并证明了不断发展的尖峰神经网络的性能要优于现有的机器学习技术。

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