Research Article Open Access

Novel Approach to Generate Signature Preventing Network Attack as Unsupervised Detection

Abstract

At present days, it is a challenging job to detect network attacks as an unsupervised detection. Various methods proposed to work the problem regarding network attack and determine a solution using specialized signatures, but technique is expensive to follow out and hard to generate labeled traffic data sets for profiling. In this study, we focus on unsupervised approach to detect new kinds of network attacks not seen before. Clustering technique is used to find out inconsistent traffic flows. Clustering algorithm is applied for constructing specific filtering rules automatically so that it can characterize different attacks as well as provides easy interpreted information to the network operator. More ever rules united to make a signature, which can directly exported/transfer towards security devices like IDSs and/or Firewalls. This approach finds different attack without knowledge of traffic. Unsupervised Network Anomaly Detection Algorithm is used for knowledge-independent detection of anomalous traffic. UNADA uses a novel clustering technique based on Sub-Space-Density clustering to identify clusters and outliers in multiple low-dimensional spaces. The evidence of traffic structure provided by these multiple clustering is then combined to produce an abnormality ranking of traffic flows, using a correlation-distance-based approach.

Priti B.Dhanke, Pratibha Mishra

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