Name
Abstract | ||
|---|---|---|
This paper presents a new approach to detect attacks from network activities. Network connections were transformed into data points in the predefined feature space. The influence function was designed to quantify the influence of an object and, further, the data field was divided into positive field and negative field according to the source point's category. To perform classification, all the labeled training samples were regarded as source points and build a data field in the feature space. When detecting, the influence felt by given testing point in this field was calculated and behaviors class was judged according to the sign and magnitude of the influence. Experimental results demonstrate that the detection performance of our approach is satisfying. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/FSKD.2008.15 | FSKD (4) |
Keywords | Field | DocType |
network connection,source point,network activity,influence function,negative field,data point,network anomaly attack detection,positive field,new approach,feature space,data field,classification algorithms,testing,active network,satisfiability,intrusion detection,classification,data mining | Data field,Magnitude (mathematics),Data mining,Computer science,Artificial intelligence,Intrusion detection system,Data point,Distance measurement,Feature vector,Pattern recognition,Influence function,Statistical classification,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.39 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyu Yang | 1 | 19 | 4.73 |
| Lixia Xie | 2 | 12 | 3.66 |
| Feng Xie | 3 | 9 | 1.55 |