Anomaly Detection Using Neural Networks
Sometimes, when we discuss our anomaly detection solutions with potential customers or analysts, we notice that there is still a lack of knowledge out there about the power and nature of intelligent anomaly detection. Often we are asked: "Is anomaly detection not just a fancy name for baselining? " Of course it is not, but it is understandable why many people would think that. After all, the first systems which claimed to be able to detect anomalies on the network were indeed based on baselines, or worse, on manually specified rules. And even today there are still systems out there, which try to utilize baselining to detect network anomalies.
We believe that this technique is very much flawed, and that an intelligent approach to anomaly detection is needed. This is why we employ artificial intelligence, by using specialized neural networks in netDeFlect, Esphion's anomaly detection solution.
But let me outline this issue in more detail.
Baselining: What is it and what are the problems?
The idea of baselining is simple: Observe the network and record certain statistics about the network. Then find averages, minima and maxima for those statistics. After that is done, continue to observe and raise an alarm if the observed statistic deviates out of the established range.
Intrusion Detection with Artificial Neural Networks: Anomaly based Intrusion Detection using Backpropagation Neural Networks
Book (VDM Verlag)
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