Cyber attack prediction on IoT and IIOT devices using ensemble based predictive analytic model

The accelerated connectivity across almost all entities of the globe has eventually escalated the number of Internet of Things (IoT) and Industrial IoT (IIoT) devices. These objects need seamless internet access which makes them vulnerable to various types of cyber-attacks at various layers. Many innovative and efficient solutions are developed for preventing these attacks and protecting the devices and data from malicious access. However, new forms of attacks continue to surge every year, which necessitates the development of more adaptive solutions by harnessing the attack profile data. The proposed research work constructs a heterogeneous ensemble model consisting of different types of base machine learning algorithms. These models explore the network profile data of the IoT and IIoT devices to extract inherent and latent features from it and predicts the network activity. Later, the predictions of the individual base learners are aggregated using a meta learner model to give the final prediction. The performance of the proposed ensemble model is validated using standard prediction metrics to assess its performance. As future research extension many base learners could be integrated to form a more efficient and powerful ensemble model with novel aggregation techniques.