Year: 2014 | Month: December | Volume 2 | Issue 2
Advances in intrusion detection systems with applications to data mining
Abstract:
With the introduction of smart phones with advanced computing and storage capabilities users experience novel kind of security threats. Conventional preventive mechanisms like encryption, authentication alone don’t seem to be enough to produce adequate security for a system. So, we tend to need sensible Intrusion detection systems which will improve security and substantially reduce the cellular phones computing resources. In this work we tend to plan an intrusion detection procedure that with efficiency detects intrusions in mobile phones with an application to Data Mining. To remove overhead of processing from the mobile phones we used network based approach. We build a neural network classifier that may be trained for every user using its call logs. Application will run on phone of the user and collects concerned data of the user and sends them over to the remote server. Results indicate the effectiveness of our methodology to observe intrusions and outperformed existing Intrusion detection strategies with about ninety five percent detection rate.
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