Query-based Backpropagation Neural Networks and its Application in Anomaly Detection
Date Issued
2011
Date
2011
Author(s)
Lai, Liang-Bin
Abstract
Anomaly detection is an important problem that has been widely studied within diverse research areas and application domains. Many anomaly detection techniques have been developed in the neural networks communities. Since anomalies are rare events (also known as imbalance data) issue occurs when there is a very small percentage of positive instances while the large number of negative instances dominates the detection model during the training process. Neural networks are a powerful technique to solve many problems; however, in many real world domains, available data sets are imbalanced. Empirical studies of the backpropagation algorithm show that the class imbalance problem generates unequal contributions to the mean square error in the training phase. Therefore, when presented with complex imbalanced data sets, this algorithm fails to represent the distributive characteristics of the data and resultantly provide unfavorable accuracies across the classes of the data.
This study attempts to overcome the problem by applying a query-based learning technique to reduce the number of training samples required and the number of feature space dimensions. To achieve this goal, we developed a new method that can improve backpropagation''s convergence time and generalization capabilities. Preserving the classification accuracy rates increase the overall execution efficiency by reducing both the uninformative training samples and the irrelevance of feature spaces. The data used in the experiments are the well-known KDD Cup 1999 intrusion detection data set and the UCI machine learning benchmark repository. The experimental result shows that, our method gives better performance in comparison with the conventional backpropagation neural networks, which combine queried samples and feature selection. The trained network has excellent performance in convergence time and generalization ability to improve imbalance problem.
Subjects
anomaly detection
neural networks
backpropagation algorithm
query-based learning
feature selection
Type
thesis
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