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International Journal of

Recent Trends in Engineering

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International Journal of Recent Trends in Engineering (IJRTE)

ISSN 1797-9617

Volume 1,  Number 1,  May 2009

Issue on Computer Science

Page(s): 133-136

Detection of Anomalous Data using Data Visualization Techniques

           Sumalatha Ramachandran, Sindhuja Vijayaraghavan, Radhika Ramadoss and Sathya       Marimuthu

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One of the most pressing problems in enforcing security in a network is the identification of suspicious nodes and messages in a network. A node's suspicion factor cannot be measured based on the information that is being sent through the network alone. Anomalous nodes are nodes in a network that send strange, malformed data only within a small instance of time. These data are crucial in cybercrime investigations and other kinds of fraudulence. Though existing methods can determine any misbehavior of nodes, little importance has been given to the field of detection of anomalous nodes – nodes that are forced to send suspicious data by the end user at few instances. As the end user is closely related to the anomalous data, even they are treated as ‘nodes’ in the study. This paper intends to present an approach based on semantic data visualization in order to identify these nodes. The plan is to provide supportive, user-friendly (preferably natural language) explanations that support and verify the anomalous nature of the predicted nodes. Hence, semantic data is being fed as the input to the proposed problem. The intended solution will also use a mechanism for automatic generation of equivalent user-friendly support-data for the detection, thereby leaving it to the end user to evaluate the veracity of the anomalous nature of the node. As initial stages consideration of the data sources that are given to the input data is made. As per the consideration, the data sources used are ASCII text sent over the LAN.

Index Terms

Anomalous data, data visualization, ontology, Tree pruning, domain ontology, intelligent systems.

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