Description

Despite the benefits of the Internet of Things (IoT), the growing influx of IoT-specific malware coordinating large-scale cyberattacks via infected IoT devices has created a substantial threat to the internet ecosystem. The inherent security requirements of Internet-based applications also highlights further the role of traffic analysis for IoT malware detection. Therefore, developing Deep Learning (ML) models, which can successfully identify network malware, is one of the most important tasks. However, among the DL models applied to network traffic classification. Selecting best feature selection for implementing chi square algorithm. Our proposed approach we are implementing CNN (convolutional neural network) give high accuracy status with accurate network malware prediction result. Also we are finding performance like precision, recall, f1score and support.