Flash Attack Prognosis by Ensemble Supervised Learning for IoT Networks

The scope of the Internet of Things (IoT) becomes inevitable in the communication and information-sharing routines of human life, similar to any technological architecture. The IoT is also not exempted from vulnerability to security issues and is even more vulnerable as the networks of IoT are built of non-smart devices. Though the few contributions endeavored to defend against the botnet's attacks on IoT, they partially or poorly performed to defend against the flash crowd or attacks by botnets on IoT networks. In this context, the method “Flash Attack Prognosis by Ensemble Supervised Learning for IoT Networks” derived in this manuscript is centric on defending the flash attacks by botnets. Unlike contemporary models, the proposed method uses the fusion of traditional network features and temporal features as input to train the classifiers. Also, the curse of dimensionality in the training corpus, which is often, appears in the corpus of flash attack transactions by a botnet, has addressed by the ensemble classification strategy. The comparative analysis of the statistics obtained from the experimental study has displayed the significance and robustness of the proposed model compared to contemporary models