부산대학교 도서관

Everything in today’s world has related to the Internet of Things (IoT) with help of sensors. Due to digitization of the machines and household products Internet of Things (IoT) has become a name worldwide today and became a necessity for every second person. IoT is effectively implemented in air conditioners, refrigerators, bulb wristwatches, washing machines, mobile phones, and almost all automated home appliances, etc. It has had a significant social impact on everyone's lives, but as the number of IoT networks grows the number of cyber-attacks also grows. Now it became imperative to secure our IoT Networks or to reduce them from cyberattacks in the form of Botnet Malware or any other malware through Machine Learning (ML) concepts or techniques. Machine Learning has played a major role in the detection of IoT botnet attacks. Most technology, including Machine Learning and Deep Learning, has been incorporated in some form or another from Artificial Intelligence (AI), Cloud Computing, and even IoT in terms of IoT network security and how we will assess and enhance network performance. This paper seeks to address the concern of security attacks on IoT Networks and use Machine Learning methods/models, specifically the K-Nearest Neighbor (KNN) algorithm and Support Vector Machine (SVM) to improve the performance of the network of IoT devices.