Deep Learning Techniques for Detecting BOTNET Attacks in IOT Environments: A Review

With the exponential growth of the Internet of Things (IoT) and its increasing integration into various domains, security threats have become a significant concern. One of the most menacing threats in the IoT landscape is botnet attacks, which can cause extensive damage and compromise the privacy and integrity of data via commands and control mechanisms. Traditional security measures are often insufficient in detecting these sophisticated attacks. This review explores the application of deep learning techniques for botnet detection in IoT environments. By analyzing the strengths and limitations of various deep learning models, the aim is to provide insights into their effectiveness and potential for securing IoT ecosystems. Thus, this study will provide better understanding of how deep learning-based models can be built using some novel approaches.