Name
Title | ||
|---|---|---|
A comparative study of fuzzy inference systems, neural networks and adaptive neuro fuzzy inference systems for portscan detection |
Abstract | ||
|---|---|---|
Worms spread by scanning for vulnerable hosts across the Internet. In this paper we report a comparative study of three classification schemes for automated portscan detection. These schemes include a simple Fuzzy Inference System (FIS) that uses classical inductive learning, a Neural Network that uses back propagation algorithm and an Adaptive Neuro Fuzzy Inference System (ANFIS) that also employs back propagation algorithm. We carry out an unbiased evaluation of these schemes using an endpoint based traffic dataset. Our results show that ANFIS (though more complex) successfully combines the benefits of the classical FIS and Neural Network to achieve the best classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-78761-7_6 | EvoWorkshops |
Keywords | Field | DocType |
propagation algorithm,comparative study,neural network,fuzzy inference system,adaptive neuro fuzzy inference,best classification accuracy,classification scheme,simple fuzzy inference system,classical inductive learning,classical fis,automated portscan detection,adaptive neuro fuzzy inference system,neural networks | Back propagation algorithm,Neuro-fuzzy,Inference,Computer science,Fuzzy inference,Classification scheme,Artificial intelligence,Adaptive neuro fuzzy inference system,Artificial neural network,Machine learning,The Internet | Conference |
Volume | ISSN | ISBN |
4974 | 0302-9743 | 3-540-78760-7 |
Citations | PageRank | References |
3 | 0.46 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Zubair Shafiq | 1 | 546 | 43.41 |
| Muddassar Farooq | 2 | 1221 | 83.47 |
| Syed Ali Khayam | 3 | 450 | 33.86 |