Name
Title | ||
|---|---|---|
A Hybrid Approach Towards Intrusion Detection Based on Artificial Immune System and Soft Computing |
Abstract | ||
|---|---|---|
A number of works in the field of intrusion detection have been based on Artificial Immune System and Soft Computing. Artificial Immune System based approaches attempt to leverage the adaptability, error tolerance, self- monitoring and distributed nature of Human Immune Systems. Whereas Soft Computing based approaches are instrumental in developing fuzzy rule based systems for detecting intrusions. They are computationally intensive and apply machine learning (both supervised and unsupervised) techniques to detect intrusions in a given system. A combination of these two approaches could provide significant advantages for intrusion detection. In this paper we attempt to leverage the adaptability of Artificial Immune System and the computation intensive nature of Soft Computing to develop a system that can effectively detect intrusions in a given network. |
Year | Venue | Field |
|---|---|---|
2012 | CoRR | Adaptability,Data mining,Artificial immune system,Computer science,Error tolerance,Anomaly-based intrusion detection system,Artificial intelligence,Soft computing,Intrusion detection system,Machine learning,Fuzzy rule based systems,Computation |
DocType | Volume | Citations |
Journal | abs/1205.4457 | 1 |
PageRank | References | Authors |
0.36 | 19 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sugata Sanyal | 1 | 481 | 65.88 |
| Manoj Rameshchandra Thakur | 2 | 14 | 2.46 |