Name
Abstract | ||
|---|---|---|
As an active and dynamic security-defense technique, intrusion detection can detect the interior and exterior attacks, and it plays an important role in assuring the network security. A radial basis function (RBF) neural network learning algorithm based on immune recognition algorithm which based on the clonal selection principle recognition principle was studied. In the algorithm, the input data was regarded as antigens, and antibodies are regarded as the hidden layer centers. The weights of the output layer are determined by adopting the Recursive least square method, which can improve convergence speed and precision of the RBF neural network. This algorithm was applied to Intrusion Detection Systems. Theory and experiment show that this algorithm has better ability in intrusion detection, and can be used to improve the efficiency of intrusion detection, reduce the false alarm rate. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CIS.2012.128 | CIS |
Keywords | Field | DocType |
convergence,learning artificial intelligence | Convergence (routing),Radial basis function,Pattern recognition,Computer science,Network security,Anomaly-based intrusion detection system,Artificial intelligence,Constant false alarm rate,Artificial neural network,Intrusion detection system,Recursion,Machine learning | Conference |
Volume | Issue | Citations |
null | null | 1 |
PageRank | References | Authors |
0.48 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Chun Peng | 1 | 1 | 1.50 |
| Yi Niu | 2 | 46 | 19.65 |
| Qiwei Hu | 3 | 1 | 1.16 |