By by Kalle Burbeck.
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Additional resources for Adaptive real-time anomaly detection for safeguarding critical networks
Data point b will be considered as part of a possible attack because it is most similar to a small cluster, assumed to be part of an attack. • Data point c can be considered as part of a new attack because it is not similar to any (large) cluster. Clustering can also be used for straightforward classification. In contrast to pure anomaly detection and unsupervised anomaly detection, labelled training data is then clustered. Each cluster can be given the class of the majority of the contained data objects for example.
For example, a message saying "telnetd: Successful login as user root" or "File changed" is classified as interesting, but messages like "Syslog-ng: 54 Objects alive. Garbage collecting while idle" or "Could not resolve host name" will be classified as uninteresting (the last message is an example of a misconfiguration that should be detected and reported separated from the intrusion detection process). The adaptive filters are used in the following workflow: 1. For performance reasons, the algorithm for adaptive filtering is launched on a periodic basis.
3 Software agents According to Russell and Norvig , an agent is “anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors”. Human agents use eyes and other organs as sensors, and hands, mouth and other body parts as effectors. Software agents exist only as encoded bit strings inside computers. g. robots). But what makes software agents different from other program components? Comparing agents to objects, two important differences in general are: • Autonomy - The object has state and so does the agent but the agent also incorporates the ability to initiate actions as what could be explained as having behaviours.