With IoT bringing a large number of day-by-day objects into the digital fold to make them smarter. It is also evident that the IoT is going to transform into a multi-trillion-dollar industry in the near future. However, the reality is that IoT bandwagon rushing full steam ahead is prone to count-less cyber- attack’s in the extremely hostile environment like the internet. Nowadays, standard PC security solutions won’t solve the challenge of privacy and data security transmitted over the internet. In this Paper, we have applied a Bidirectional Recurrent Neural Network to build a security solution with high durability for IoT network security. DL and ML have shown remarkable result in dealing with multimodal and voluminous hetero-generous data in regard’s to intrusion detection especially with the architectures of Recurrent Neural Network’s. Feature selection mechanism were also implemented to help identify and remove non-essential variables from data that does not affect the accuracy of the prediction model. In this case a Random Forest algorithm was implemented over Principal Component Analysis because of flexibility, and easy in using machine learning algorithms that allow production without hyper-parameter tuning, building of multiple decision tree and merging them together to get a more accurate and stable prediction. In this study a novel algorithm (BRNN) out-performed both Recurrent Neural Network and Gated Recurrent Neural Network because it consider both information from the past and the future with back and forward hidden neuron’s.

IoT, Recurrent Neural Network’s, Bi-Directional RNN, Intrusion Detection, Deep Learning, Machine Learning.

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