This chapter conducts a comprehensive empirical review of internet of things (IoT) botnet detection to identify gaps in the literature. An empirical analysis of literature work related to IoT botnet detection is conducted. A state-of-the-art review of works done on IoT botnet detection is synthesized. This review is based on classifying the subcategories of IoT botnet detection, including honeypot and intrusion detection techniques, specifically host and network-based IDSs. This is further broken down into anomaly, signature, and hybrid-based approaches. Anomaly-based detections include machine learning techniques and deep learning techniques. Other detection methods include distributed techniques (software defined networking [SDN] and blockchain), graph theory approach, and domain name service (DNS) techniques. Finally, the chapter recommends future research directions in IoT security and the application of deep learning techniques.