The dramatic increase in the number of Internet of Things (IoT) devices has created rapid growth for exploitation of security flaws and vulnerabilities. Particularly for critical infrastructure and real-time systems security threats can be highly damaging. Machine Learning (ML) algorithms have demonstrated the ability to combat the security threats and improve the efficiency of data management within IoT networks. This paper addresses how ML methods improve security and efficiency. A review of the current approaches is conducted and these approaches are categorized into detection systems as well as privacy and efficiency enhancements. The proposed future research directions are then presented to address the limitations of the state-of-the-art ML-based IoT security methods.