Predicting database intrusion with an ensemble of neural networks: A Time Series Approach

Title:

Predicting database intrusion with an ensemble of neural networks: A Time Series Approach

Creator:

Ramasubramanian , P. and Kannan, A.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:5cba7302-ec0f-439e-9cac-0075b1970c5a
uuid:5cba7302-ec0f-439e-9cac-0075b1970c5a

Subject:

Database security, database anomaly intrusion prediction, Quickprop prediction technique, intrusion prevention, artificial neural networks, and uncertainty

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper describes a framework for a statistical anomaly prediction system using Quickprop neural network ensemble forecasting model, which predicts unauthorized invasions of users based on previous observations and takes further action before intrusion occurs. This paper investigates a NN ensemble approach to the problem of intrusion prediction and the various architectures are investigated using Quickprop algorithm. This paper focuses on intrusion prediction techniques for preventing intrusions that manifest through anomalous changes in intensity of transactions in a relational database systems at the application level. We present a novel approach to prevent misuse within an information system by gathering and maintaining knowledge of the behavior of the user rather than anticipating attacks by unknown assailants. The experimental study is performed using real data provided by a major Corporate Bank. A comparative evaluation of the two ensemble networks over the individual networks was carried out using a mean absolute percentage error on a prediction data set and a better prediction accuracy has been observed. Furthermore, the performance analysis shows that the model captures well the volatility of the user behavior and has a good forecasting ability.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2005 Volume:15 | Number:1

Harvested from:

CDK

Metadata only:

false