Name
Title | ||
|---|---|---|
Feature Weighting and Selection for a Real-Time Network Intrusion Detection System Based on GA with KNN |
Abstract | ||
|---|---|---|
A good feature selection policy which can choose significant and as less as possible features plays a key role for any successful NIDS. The paper presents a genetic algorithm combined with kNN (k-Nearest Neighbor) for feature weighting. We weight all initial 35 features in the training phase and then select tops of them to implement a NIDS for testing. Many DoS/DDoS attacks are applied to evaluate the system. For known attacks we can get the best 97.42% overall accuracy rate while only the top 19 features are considered; as for unknown attacks, we can get the best 78% overall accuracy rate by top 28 features. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-69304-8_20 | ISI Workshops |
Keywords | Field | DocType |
known attack,possible feature,feature weighting,k-nearest neighbor,genetic algorithm,ddos attack,overall accuracy rate,real-time network intrusion detection,key role,good feature selection policy,successful nids,real time,network security,k nearest neighbor,feature selection | Data mining,Weighting,Feature selection,Denial-of-service attack,Computer science,Network security,Real time networks,Artificial intelligence,Intrusion detection system,Machine learning,Genetic algorithm,TOPS | Conference |
Volume | ISSN | Citations |
5075 | 0302-9743 | 6 |
PageRank | References | Authors |
0.50 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-Yang Su | 1 | 362 | 22.26 |
| Kai-Chi Chang | 2 | 16 | 2.94 |
| Hua-Fu Wei | 3 | 8 | 1.27 |
| Chun-Yuen Lin | 4 | 41 | 3.71 |