Network Intrusion Detection using Data Mining Technique

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Provided by: International Journal of Advanced Research in Computer Engineering & Technology
Topic: Big Data
Format: PDF
In recent years, most of the research has been done in the field of Intrusion Detection System (IDS) to detect attacks in network traffic indicating malicious activity. IDS can be built in two different ways: signature based and anomaly based. This paper presents anomaly based network intrusion detection model using Density Based Spatial Clustering for Applications with Noise (DBSCAN) technique called DNIDS (DBSCAN based Network Intrusion Detection System) for the identification of abnormality in the network traffic dataset.
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