TY - JOUR T1 - A Novel Data Mining based Hybrid Intrusion Detection Framework AU - MradulDhakar and Akhilesh Tiwari JO - Journal of Information and Computing Science VL - 1 SP - 037 EP - 048 PY - 2024 DA - 2024/01 SN - 9 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22595.html KW - Data Mining, Intrusion Detection, Classification, K2, TAN, REP, KDDCup’99 AB - 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 intrusive activities, several network(s) from 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.

MradulDhakar and Akhilesh Tiwari. (2024). A Novel Data Mining based Hybrid Intrusion Detection Framework. Journal of Information and Computing Science. 9 (1). 037-048. doi: