Securing Iot Networks Against Botnet Attacks with Machine Learning and Deep Learning Techniques

The rapid-fire proliferation of Internet of effects (IoT) biashas introduced unknown openings for connectivity and invention, but it has also exposed significant security vulnerabilities. IoT bias,frequently constrained by limited computational coffers and weak security measures, are decreasingly targeted by cyber criminals to form botnets for executing large-scale Distributed Denial of Service (DDoS)attacks, data breaches, and other vicious conditioning. This paper investigates the operation of machine literacy (ML) and deep literacy (DL) styles to address these pitfalls and secure IoT networks against botnet attacks. ML and DL ways offer adaptive, scalable, and automated results for real-time discovery, forestallment, and mitigation of botnet conditioning. By using anomaly discovery, point engineering and advanced neural network infrastructures similar as intermittent and convolutional networks,the sestyles demonstrate significant eventuality in relating and negativing botnet intrusions. The paper also explores cold-blooded approaches,challenges inperpetration, and unborn directions for developing robustand effective IoTsecurity fabrics.