神经网络和入侵检测系统
我如何使用神经网络和入侵检测系统来解决这个问题，假设我们通过 FTP 进行了攻击。
假设有人尝试通过对 ftp 帐户进行暴力攻击来不断尝试不同的登录。
我该如何设置神经网络的结构？我必须考虑哪些事情？它将如何识别“未来的类似方法”？
任何图表和输入将不胜感激。
How do I approach the problem with a neural network and a intrusion detection system where by lets say we have an attack via FTP.
Lets say some one attempts to continuously try different logins via brute force attack on an ftp account.
How would I set the structure of the NN? What things do I have to consider? How would it recognise "similar approaches in the future"?
Any diagrams and input would be much appreciated.
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你的问题非常笼统，一个好的答案本身就是一个项目。我建议聘请具有神经网络设计经验的人来帮助提出合适的模型，甚至告诉您您的问题是否适合使用神经网络。不过，有一些想法：
输入需要量化，因此首先列出可以测量的可能的数字输入。
输出也需要量化，您可能无法生成简单的“是/否”响应。您很可能想要生成一个或多个数字来表示攻击的粗略概率，可能按类别细分。
您需要积累大量训练数据，这些数据已被分析并量化为您设计的输入和输出。弄清楚执行此量化的过程是整个问题的重要组成部分。
您还需要大量验证数据，这些数据应该以与训练数据相同的方式进行量化，但不应参与训练，否则您将简单地强制建立一个相关网络，该网络可能会完全没有意义。
完成上述操作后，您可以考虑如何构建网络以及要用于训练网络的具体算法。关于这个主题有很多文献，但老实说，这是问题中比较简单的部分。 以可以连贯处理的方式表示问题要困难得多。
Your question is extremely general and a good answer is a project in itself. I recommend contracting someone with experience in neural network design to help come up with an appropriate model or even tell you whether your problem is amenable to using a neural network. A few ideas, though:
Inputs need to be quantized, so start by making a list of possible numeric inputs that you could measure.
Outputs also need to be quantized and you probably can't generate a simple "Yes/no" response. Most likely you'll want to generate one or more numbers that represent a rough probability of it being an attack, perhaps broken down by category.
You'll need to accumulate a large set of training data that has been analyzed and quantized into the inputs and outputs you've designed. Figuring out the process of doing this quantization is a huge part of the overall problem.
You'll also need a large set of validation data, which should be quantized in the same way as the training data, but that should not take any part in the training, as otherwise you will simply force a correlation network that may well be completely meaningless.
Once you've completed the above, you can think about how you want to structure your network and the specific algorithms you want to use to train it. There is a wide range of literature on this topic, but, honestly, this is the simpler part of the problem. Representing the problem in a way that can be processed coherently is much more difficult.