A Novel and Advanced Data Mining Model based Hybrid Intrusion Detection Framework - K. Rajasekaran; K. Nirmala (Indonesian Journal of Electrical Engineering and Computer Science, 2015)
An Intrusion can be defined as any practice or act that attempt to crack the integrity, confidentiality or availability of a resource. This may contain of a deliberate unauthorized attempt to access the information, manipulate the data, or make a system unreliable or unusable. With the expansion of computer networks at an alarming rate during the past decade, security has become one of the serious issues of computer systems. 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. DOI:  http://dx.doi.org/10.11591/telkomnika.v13i2.7045