Distributed detection of anomalies Sample Clauses

Distributed detection of anomalies. The classical setting in networking generally assumes that the information that is exchanged is statistically independent. However very frequently this is not strictly the case and nodes do exchange information that is correlated. Distributed anomaly detection has to deal precisely with such a setting: each monitoring node in the network maintains a vector of numerical state information. As the different monitoring nodes observe interacting traffics the state maintained by them is correlated. Distributed monitoring and anomaly detection involves exchanging information between nodes such that each node obtains an approximate view of the states of all other nodes. Through the information exchanged the local observation of a node is extended to the global state of the network. One important challenge here is to deal with nodes selfishness, i.e. a node wants to achieve the best approximation of other nodes states consuming itself the lowest amount of resources. This means that we need an incentive/punishment cooperative mechanism to motivate node to exchange information. One should also assume that the quality of approximation about other states is not defined a priori; it needs to be defined online during the operation of the distributed system. The distributed anomaly detection, a communication scheme initially designed by Lancaster University enables the sharing of correlated node states; a punishment scheme has been proposed that solves the node selfishness issue; a signalling method has been proposed that enables a node to announce to other nodes its state variable of interest as well as their importance. The proposed scheme merges approximation obtained at different nodes into a single consistent approximation with better quality. We will show that the proposed scheme implements a negotiation between neighbours that trade-off an increase of the node transmission rate to its neighbour with a mechanism achieving a better approximation of the state of other nodes.
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Distributed detection of anomalies. The class of parametric statistical anomaly detectors needs to implement three phases:
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Distributed detection of anomalies. 5.3.2.1 Performance objectives, and evaluation criteria The aim is distributed anomaly detection is to achieve the best detection rate vs. false alarm trade-off. However, because of the distributed nature of our approach, we add another dimension that is the cost of the detection in term of information exchange rate between nodes.
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Distributed detection of anomalies. The integration of the distributed anomaly detection component into the global ECODE architecture is taking advantage of all parts of the architecture. Two major parts of the overall ECODE architecture cooperate during the distributed anomaly detection: the MLE (machine learning engine) and the ME (Monitoring Engine). However there is a major interaction between distant MLE in our case.
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Related to Distributed detection of anomalies

  • Reporting Requirement (1) In the event the Contractor identifies covered telecommunications equipment or services used as a substantial or essential component of any system, or as critical technology as part of any system, during contract performance, or the Contractor is notified of such by a subcontractor at any tier or by any other source, the Contractor shall report the information in paragraph (d)(2) of this clause to the Contracting Officer, unless elsewhere in this contract are established procedures for reporting the information; in the case of the Department of Defense, the Contractor shall report to the website at xxxxx://xxxxxx.xxx.xxx. For indefinite delivery contracts, the Contractor shall report to the Contracting Officer for the indefinite delivery contract and the Contracting Officer(s) for any affected order or, in the case of the Department of Defense, identify both the indefinite delivery contract and any affected orders in the report provided at xxxxx://xxxxxx.xxx.xxx.

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