The term internet of things is a buzz word these days and as per Google survey conducted recently, it has even dominated the buzz word big data predominantly. However, IoT area is still not matured and is throwing light on lot of research issues towards the data mining researchers. Security in IoT throws several challenges because of limited resources. In this context, IoT gains importance once again from data miners towards anomaly mining or intrusion detection. Intrusion detection is classified as NP-class in the literature even today. Algorithms addressing privacy and security issues in IoT must consider the complexities involved and hence require re-attention from all researchers. One more problem faced when judging for intrusion is the use of high dimensionality, classifier choice, and distance measure. For example, the traditional distance measure, such as Euclidean misjudges the similarity. In this paper, the objective is to design a fuzzy membership function to address both dimensionality and anomaly mining so as reduce the computational complexity and increase computational accuracies of classifier algorithms. We validate the proposed measure using several experimentations on NSL-KDD and DARPA datasets using kNN, J48 and CANN using Gaussian measure. Improved accuracies of classifiers on U2R and R2L attacks have been recorded in the experimental results obtained for experiments conducted.
['Rajesh Kumar Gunupudi', 'Mangathayaru Nimmala', 'Narsimha Gugulothu', 'Suresh Reddy Gali']
CLAPP: A self constructing feature clustering approach for anomaly detection