There are a number of research challenges associated with Internet of Things (IoT) security, and one of these challenges is to design novel frameworks to mine malicious frequent patterns for identifying misuse and detecting anomalies without incurring high computational costs (e.g. due to generation and analysis of unnecessary patterns and gap creation between patterns). Association rule mining is a popular approach in the literature; hence, in this paper, we critically analyze existing association rule mining techniques. We then present a framework for mining malicious frequent patterns in an IoT deployment, prior to evaluating the utility of the proposed framework using data from a Pakistan-based organization.