Dr. Subhash C Bagui

In this paper, we use three different machine learning classifiers in spark, decision tree, random forest, and logistic regression, to classify attack traffic of different types of IoT devices from the Kitsune dataset. Kitsune allows us to use real-time network traffic information from data streams to dynamically generate features in real time. In this work, only protocol usage statistics generated from pcap files of the original data streams is used to detect malicious traffic in real time using the Big Data framework. Performance is measured in terms of accuracy, attack detection rate (ADR), false alarm rate (FAR), and runtime.

This research presents a way to identify attribute-value relationships already existing in a database by using association rule mining to optimize query processing. Once relationships have been determined, these relationships can be used as a basis for creating temporary structures like views to optimize query operations. This paper presents an architecture that shows how table partitions in the form of views, created based on association rules, can be used to optimize queries. The results of this study were statistically significant.