A deep learning approach for intrusion detection in Internet of Things using bi-directional long short-term memory recurrent neural network

Internet-of-Things connects every ‘thing’ with the Internet and allows these ‘things’ to communicate with each other. IoT comprises of innumerous interconnected devices of diverse complexities and trends. This fundamental nature of IoT structure intensifies the amount of attack targets which might affect the sustainable growth of IoT. Thus, security issues become a crucial factor to be addressed. A novel deep learning approach have been proposed in this thesis, for performing real-time detections of security threats in IoT systems using the Bi-directional Long Short-Term Memory Recurrent Neural Network (BLSTM RNN). The proposed approach have been implemented through Google TensorFlow implementation framework and Python programming language. To train and test the proposed approach, UNSW-NB15 dataset has been employed, which is the most up-to-date benchmark dataset with sequential samples and contemporary attack patterns. This thesis work employs binary classification of attack and normal patterns. The experimental result demonstrates the proficiency of the introduced model with respect to recall, precision, FAR and f-1 score. The model attains over 97% detection accuracy. The test result demonstrates that BLSTM RNN is profoundly effective for building highly efficient model for intrusion detection and offers a novel research methodology

A deep learning approach for intrusion detection in Internet of Things using bi-directional long short-term memory recurrent neural network

Internet of Things (IoT) is one of the most rapidly evolving technologies nowadays. It has its impact in various industrial sectors including logistics tracking, medical fields, automobiles and smart cities. With its immense potentiality, IoT comes with crucial security concerns that need to be addressed. In this paper, we present a novel deep learning technique for detecting attacks within the IoT network using Bi-directional Long Short-Term Memory Recurrent Neural Network (BLSTM RNN). A multi-layer Deep Learning Neural Network is trained using a novel benchmark data set: UNSWNB15. This paper focuses on the binary classification of normal and attack patterns on the IoT network. The experimental outcomes show the efficiency of our proposed model with regard to precision, recall, f-1 score and FAR. Our proposed BLSTM model achieves over 95% accuracy in attack detection. The experimental outcome shows that BLSTM RNN is highly efficient for building high accuracy intrusion detection model and offers a novel research methodology