A Novel Data Mining based Hybrid Intrusion Detection Framework

The prosperity of technology worldwide has made the concerns of security tend to increase rapidly. The enormous usage of internetworking has raised the need of protecting system(s) as well as network(s) from the unauthorized access (intrusion). To tackle the intrusive activities, several countermeasures have been found in literature viz. firewall, antivirus and currently widely preferred Intrusion detection System (IDS). IDS, is a detection mechanism for detecting the intrusive activities hidden among the normal activities. The revolutionary establishment of IDS has attracted analysts to work dedicatedly enabling the system to deal with technological advancements. Hence in this regard, various beneficial schemes and models have been proposed in order to achieve enhanced IDS. This paper proposes a novel hybrid model for intrusion detection. The proposed framework in this paper may be expected as another step towards advancement of IDS. The framework utilizes the crucial data mining classification algorithms beneficial for intrusion detection. The Hybrid framework would henceforth, will lead to effective, adaptive and intelligent intrusion detection.