Securing Consumer Internet of Things for Botnet Attacks: Deep Learning Approach

DDoS attacks in the Internet of Things (IoT) technology have increased significantly due to its spread adoption in different industrial domains. The purpose of the current research is to propose a novel technique for detecting botnet attacks in user-oriented IoT environments. Conspicuously, an attack identification technique inspired by Recurrent Neural networks and Bidirectional Long Short Term Memory (BLRNN) is presented using a unique Deep Learning (DL) technique. For text identification and translation of attack data segments into tokenized form, word embedding is employed. The performance analysis of the presented technique is performed in comparison to the state-of-the-art DL techniques. Specifically, Accuracy (98.4%), Specificity (98.7%), Sensitivity (99.0%), F-measure (99.0%) and Data loss (92.36%) of the presented BLRNN detection model are determined for identifying 4 attacks over Botnet (Mirai). The results show that, although adding cost to each epoch and increasing computation delay, the bidirectional strategy is more superior technique model over different data instances.