Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/4039 2019 2019 Department of Computer Science, University of Pretoria, South Africa Innocent Innocent Department of Computer Science, University of Pretoria, South Africa Jan H. P. Eloff Over the years, exciting new technologies such as the Internet of Things (IoT) have changed many aspects of our lives, including smart homes. Unfortunately, this technology is vulnerable to cyber attacks owing to the lack of physical boundaries to ensure safety, privacy, and security. Botnet attacks are among the prominent cybersecurity threats because they can compromise the entire network with cyber attacks, such as distributed denial-of-service (DDoS) attacks. Hence, the intelligent discovery of new unknown botnet attacks remains a challenge, particularly in IoT environments, owing to the complex nature of the signatures of unknown botnet attacks. Through a systematic literature review, we provide a comprehensive review of current studies to determine the trends and challenges in the discovery of unknown botnet attacks. This study implemented a lightweight intelligent data-driven methodology called CySecML to discover unknown botnet attacks. The CySecML methodology differs from existing methods because of its unique data preparation and feature selection methods, specifically aimed at mitigating cyber attacks. The effectiveness of this methodology is demonstrated using state-of-the-art botnet attack data sets, where the self-training machine-learning algorithm achieved the best results with an F1-score of 94%. 2026 2026 113 134 10.54216/JCIM.170209 https://www.americaspg.com/articleinfo/2/show/4039