Intrusion detection systems (IDSs) are designed to distinguish normal and intrusive activities. A critical part of the IDS design depends on the selection of informative features and the appropriate machine learning technique. In this paper, we investigated the problem of IDS from these two perspectives and constructed a misuse based neurotree classiffier capable of detecting anomalies in networks. The major implications of this paper are a) Employing weighted sum genetic feature extraction process which provides better discrimination ability for detecting anomalies in network trafic; b) Realizing the system as a rule-based model using an ensemble efficient machine learning technique, neurotree which possesses better comprehensibility and generalization ability; c) Utilizing an activation function which is targeted at minimizing the error rates in the learning algorithm. An extensive experimental evaluation on a database containing normal and anomaly trafic patterns shows that the proposed scheme with the selected features and the chosen classiffier is a state-of-the-art IDS that outperforms previous IDS methods.
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.