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Deep recurrent neural network for IoT intrusion detection system - 2020

Deep Recurrent Neural Network For Iot Intrusion Detection System

Research Area:  Machine Learning


As a results of the large scale development of the Internet of Things (IoT), cloud computing capabilities including networking, data storage, management, and analytics are brought very close to the edge of networks forming Fog computing and enhancing transferring and processing of tremendous amount of data. As the Internet becomes more deeply integrated into our business operations through IoT platform, the desire for reliable and efficient connections increases as well. Fog and Cloud security is a topical issue associated with every data storage, managing or processing paradigm. Attacks once occurred, have ineradicable and disastrous effects on the development of IoT, Fog, Cloud computing. Therefore, many security systems/models have been proposed and/or implemented for the sake of Fog security. Intrusion detection systems are one of the premier choices especially ones that designed using artificial intelligence. In our paper, we presented an artificially full-automated intrusion detection system for Fog security against cyber-attacks. The proposed model uses multi-layered of recurrent neural networks designed to be implemented for Fog computing security that is very close to the end-users and IoT devices. We demonstrated our proposed model using a balanced version of the challenging dataset: NSL-KDD. The performance of our model was measured using a variety of typical metrics, and we add two additional metrics: Mathew correlation and Cohens Kappa coefficients for deeper insight. where the experimental results and simulations proved the stability and robustness of the proposed model in terms of a variety of performance metrics.


Author(s) Name:  Muder Almiani,Alia AbuGhazleh,Amer Al-Rahayfeh,Saleh Atiewi,Abdul Razaque

Journal name:  Simulation Modelling Practice and Theory

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.simpat.2019.102031

Volume Information:  Volume 101, May 2020, 102031