Online ISSN: 2515-8260

Keywords : Generative Mobile data stream modeling


AN EFFICIENT IOT BASED ALERT INTRUSION SYSTEM FOR GENERATIVE MOBILE DATA STREAM APPLICATION

Dr. Lokesh P Gagnani; Ramesh S; R. Senthil; Krishnakumar V; Dr. SyedKhasim

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3210-3218

IOT Alert based aggregation is an important subtask of intrusion detection. The goal is to
identify and to cluster different alert produced by low-level intrusion detection systems,
firewalls, etc.belonging to a specific attack instance which has beeninitiated by an attacker at a
certain point in time. Thus, meta-alerts can be generated for the clusters that contain all the
relevantinformation whereas the amount of data (i.e., alerts) can be reduced substantially. Metaalerts
may then be the basis for reporting tosecurity experts or for communication within a
distributed intrusion detection system. This method proposes a novel technique for online
alertaggregation which is based on a dynamic, probabilistic model of the current attack situation.
Basically, it can be regarded as a datastream version of a maximum likelihood approach for the
estimation of the model parameters.It describes the problem of intrusion detection in detail and
analyze various well known methods for intrusion detection with respect to two critical
requirements using SparkV Dataset.