By by Kalle Burbeck.
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Extra info for Adaptive real-time anomaly detection for safeguarding critical networks
The idea is to step by step improve the clusters by maximizing intra–cluster similarity, while minimizing inter–cluster similarity. One classical example is the K-means  clustering algorithms which represent each cluster by its mean, or centroid. The initial partitioning is created by randomly selecting k objects to represent the cluster means. All other objects are then assigned to the most similar cluster after which all cluster means are recomputed. This process iterates until a criterion function, such as the squared error criterion, converges.
The network health is computed as a weighted sum of all services provided by the network. The HMI agent described in the next section can then show network health as well as status for individual hosts. 5 Human-machine interface agent The Safeguard agents’ autonomous actions are carried out under the supervision of a human operator. In some situations the agents will need to ask human operators for permission to carry out actions. 3. 12: Network overview as presented by the HMI agent. unable to take action and therefore alerts or requests for help need to be sent to the human operators.
1 33 Telecommunications vulnerabilities In most European countries traditional circuit-switched telecommunication networks are being replaced by packet-based IP networks. It is therefore the manifestation of attacks, failures and accidents in IP networks that constitutes the most significant threat to telecommunications infrastructures. In recent years the dependability of IP infrastructures has decreased, due to the proliferation of worms, denial of service attacks, buffer overflow vulnerabilities and viruses.
Adaptive real-time anomaly detection for safeguarding critical networks by by Kalle Burbeck.