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A Hybrid Data Mining Model for Intrusion Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 16 |
| Year of Publication: 2021 |
| Authors: Mahreen Nasir |
10.5120/ijca2021921489
|
Mahreen Nasir . A Hybrid Data Mining Model for Intrusion Detection. International Journal of Computer Applications. 183, 16 ( Jul 2021), 14-19. DOI=10.5120/ijca2021921489
Abstract
Network intrusion detection requires analysis of network data streams for identification of possible attacks. An Intrusion Detection System (IDS) is used to analyse such attacks and prevent future attacks. Main categories of IDS are anomaly detection and misuse detection. The limitation of anomaly based detection is high false positive rate whereas misuse detection based systems can only deal with known attack types. To address these, the main contribution of this paper is to propose a framework using hybrid approach based on clustering and classification methods for Intrusion Detection (CCID).
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