Name
Abstract | ||
|---|---|---|
This paper investigates the approaches of using an analogy of the Human Immune System (HIS) to create an Artificial Immune System (AIS) based Intrusion Detection System (IDS). The two most popular AIS generating algorithms, Negative and Clonal Selection were explored and tested on the NSL-KDD dataset with different sets of features and different numbers of detectors. The experiments show that the Negative Selection Algorithm (NSA) and the Clonal Selection Algorithm (CSA) show a severe scaling issue when handling real network traffic. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BigDataService.2018.00051 | 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService) |
Keywords | Field | DocType |
artificial immune system,Negative Selection Algorithm,Clonal Selection Algorithm,intrusion detection system,Human Immune System analogy,AIS generating algorithms,NSL-KDD dataset,severe scaling issue | Data mining,Artificial immune system,Computer science,Negative selection algorithm,Artificial intelligence,Statistical classification,Clonal selection algorithm,Intrusion detection system,Clonal selection,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-5120-9 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Hooks | 1 | 0 | 0.34 |
| Xiaohong Yuan | 2 | 169 | 26.72 |
| Kaushik Roy | 3 | 2 | 2.38 |
| Albert C. Esterline | 4 | 28 | 16.82 |
| Joaquin Hernandez | 5 | 0 | 0.34 |