cruisegugl.blogg.se - Anomaly detection

The device interface tells how traffic is being utilized by the network device.

Class of Service examines the priority of the traffic.

Ports characterize the application utilizing the traffic.

Destination address tells who is receiving the traffic.

Source address allows the understanding of who is originating the traffic.

This flow information is extremely useful for understanding network behavior for a number of reasons: This methodology of fingerprinting, or determining, a flow is scalable because a large amount of network information is condensed into a database of information called the cache.Ĭomputer network traffic analysis, therefore, is based upon collecting and analyzing IP flows to determine the characteristics of network communication that is taking place. IP Packet attributes from an IP flow are:Īll packets with the same source/destination IP address, source/destination ports, protocol interface and Class of Service are grouped into a flow. Traditionally, an IP Flow is based on a set of five to seven IP packet attributes. These attributes are the IP packet identity, or fingerprint, of the packet and determine if the packet is unique or similar to other packets. Network traffic analysis, based on the IETF Internet Protocol Flow and Information Export (IPFIX) protocol, is perhaps an underutilized tool that can help identify unwanted behavior by advanced malware inside the network despite the encryption advanced malware uses to conceal itself.Ĭomputer network traffic analysis, or network flow analysis, is a method of network traffic analysis based upon the concept of an Internet Protocol Flow (IP Flow).Įach packet that is forwarded within a router or switch is examined for a set of IP packet attributes.

When tens of thousands of customer transaction records and credit cards are streaming from a database and out through the firewall, isn't that something that should be noticed and stopped? Network traffic analysis with IP Flow Instead, security tools must now focus on the interior of the network and possess network anomaly detection capabilities.Īn expanded focus means identifying all mission-critical information assets (as identified through an IT risk assessment) and then monitoring these assets to detect unwanted behavior. The planning assumption has to be made that it is not possible to detect and deny all advanced malware threats at the border. Security teams must go beyond trying to detect and deny malware at the border. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).As a result, a change of focus is needed. We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder.

We propose a novel approach for anomaly detection in High The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components.