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Application layer proxy detection, prevention with predicted load optimization

机译:应用层代理检测,通过预测负载优化进行预防

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In this paper, we have formulated a solution for proxy usages in a network. We all are surrounded with digital signatures around us, we just need to filter those to make our network clean, secure, efficient. Is what we did in this research work. There is also a great need of load optimization at different points in the network. We have proposed a way to use machine learning and neural networks to apply it on a network. Our basic approach to do this work is a time quantum analysis for load prediction over a network and digital signature validation for proxy detection at the application layer. There are many methods to detect network statistics and security at different layers, but we need real-time analysis in the network and prevention measure right before it happens.
机译:在本文中,我们为网络中的代理使用制定了解决方案。我们周围都环绕着数字签名,我们只需要过滤那些数字签名即可使我们的网络清洁,安全,高效。这就是我们在这项研究工作中所做的。还非常需要在网络中的不同点进行负载优化。我们提出了一种使用机器学习和神经网络将其应用于网络的方法。我们执行此工作的基本方法是时间量子分析,用于通过网络进行负载预测,数字签名验证用于在应用程序层进行代理检测。有许多方法可以检测不同层的网络统计信息和安全性,但是我们需要对网络进行实时分析并采取预防措施,然后才能进行检测。

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