Name
Abstract | ||
|---|---|---|
This paper presents an anomaly detection approach for the network intrusion detection based on Cellular Neural Networks (CNN) model. CNN has features with multi-dimensional array of neurons and local interconnections among cells. Recurrent Perceptron Learning Algorithm (RPLA) is used to learn the templates and bias in CNN classifier. Experiments with KDD Cup 1999 network traffic connections which have been preprocessed with methods of features selection and normalization have shown that CNN model is effective for intrusion detection. In contrast to back propagation neural network, CNN model exhibits an excellent performance owing to the higher attack detection rate with lower false positive rate. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11902140_94 | ISCIS |
Keywords | Field | DocType |
higher attack detection rate,cnn classifier,anomaly intrusion detection approach,anomaly detection approach,cellular neural network,network intrusion detection,network traffic connection,propagation neural network,cnn model,features selection,lower false positive rate,intrusion detection,data mining,cellular neural networks,intrusion detection system,feature selection,false positive rate,anomaly detection | Anomaly detection,False positive rate,Pattern recognition,Computer science,Artificial intelligence,Backpropagation,Classifier (linguistics),Artificial neural network,Perceptron,Intrusion detection system,Cellular neural network,Distributed computing | Conference |
Volume | ISSN | ISBN |
4263 | 0302-9743 | 3-540-47242-8 |
Citations | PageRank | References |
3 | 0.50 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongxue Yang | 1 | 12 | 2.37 |
| Adem Karahoca | 2 | 97 | 15.26 |