Multilayer feedforward networks are universal approximators
Neural Networks
Instance-Based Learning Algorithms
Machine Learning
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A global optimum approach for one-layer neural networks
Neural Computation
Handling Nominal Features in Anomaly Intrusion Detection Problems
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Integrated expert system applied to the analysis of non-technical losses in power utilities
Expert Systems with Applications: An International Journal
AI based supervised classifiers: an analysis for intrusion detection
ACAI '11 Proceedings of the International Conference on Advances in Computing and Artificial Intelligence
LEFT-logical expressions feature transformation: a framework for transformation of symbolic features
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
| Hi-index | 12.05 |
The success of any Intrusion Detection System (IDS) lies in the selection of a set of significant features, that can be quantitative or qualitative, taken out from a network traffic data stream. The machine learning methods provide potential solutions for the IDS problem. However, most of these methods used for classification are not able to handle symbolic attributes directly. In this paper, three methods for symbolic features conversion - indicator variables, conditional probabilities and the Separability Split Value method - are contrasted with the arbitrary conversion method, all of them applied to an intrusion detection problem, the KDD Cup 99 data set. In particular, three classification methods were subsequently applied to the dataset: a one-layer feedforward neural network, a support vector machine and a multilayer feedforward neural network. The results obtained demonstrate that the three conversion methods improve the prediction ability of the classifiers utilized, with respect to the arbitrary and commonly used assignment of numerical values.