Authors - S.Prince Samuel, R.kiruba, P.Kingston Stanley, R.Karthick Abstract - The Internet of Things (IoT) has seen an increase in cyber attacks, especially botnet attacks, mainly brought on by weak security on networks. As a result of the rise in IoT users, the requirement for electronic data interchange, and the desire for virtual services, the frequency of cyberattacks to gain access to private data has increased in recent years. As a result, industry and researchers have given the security of IoT applications and particular data attention. A botnet is a formally organized group of infected, internet-connected devices managed by cybercriminals. Attacks from botnets, which spread spam and viruses and are no longer under the control of authorized users, can damage IoT devices. To effectively detect botnet attacks, proposed a botnet attacks detection system based on Transfer Learning (TL). The transfer learning (TL) model is built upon convolutional neural networks (CNNs), which are widely used for their effectiveness in feature extraction and pattern recognition in complex datasets. For the existing model achieved 91.93%, the proposed botnet attacks detection model performed with over 99.54% accuracy on two well-known public benchmark IoT security datasets: CICIDS2017 and UNSW-NB 15. This shows the proposed model’s effectiveness in predicting botnet attacks in an IoT environment.
