Online Intrusion Alert Aggregation with Generative Data Stream Modeling
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2014, Vol 4, Issue 7
Abstract
Online intrusion alert aggregation with generative data stream modeling is a approach which uses generative modeling. It also use a method called as probabilistic methods. It can be assume that instances of an attack is similar as a process may be a random process which is producing alerts. This paper aims at collecting and modeling these attacks on some similar parameters, so that attack from beginning to completion can be identified. This collected and modeled alerts is given to security personnel to estimate conclusion and take relative action. With some data sets, we show that it is easy to deduct number of alerts and count of missing meta alerts is also extremely low. Also we demonstrate that generation of meta alerts having delay of only few seconds even after first alert is produced already.
Authors and Affiliations
Kothawale Ganesh S , Borhade Sushama R , Prof. B. Raviprasad
Keywords
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