Design of Trust Model For Efficient Cyber Attack Detection on Fuzzified Large Data using Data Mining techniques

(IDS), is being used as the main security defending technique. It is second guard for a network after firewall. Various approaches from data mining to soft computing fields have been used for detecting the misuse and anomaly attacks from the network. A significant challenge in providing an effective defense mechanism to a network perimeter is having the ability to detect intrusions and implement countermeasures. Components of the network perimeter defense capable of detecting intrusions are referred to as Intrusion Detection Systems (IDS). In this paper after studying the research done in the field we proposed a model in which first dimension reduction technique is applied. Secondly on reduced data set fuzzification of feature values is done to get simpler range. Thirdly combination of clustering and naïve based approach,on the large test dataset applied. Obtained results are analyzed on detection rate, False positive rate and precision rate. Fuzzy logic based discretization of the dataset has helped to improve the training data representation, and performs well in terms of detecting attacks faster and with reasonable reduction in false alarm rate