An IoT security framework using deep/machine learning techniques for smart offices

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A smart office is a work-place where information and communication technologies are used to increase employee productivity and optimize resource usage. The emerging IoT (Internet of Things) based smart office automation (SOA) applications include, but are not limited to, energy conservation, physical security, and smart heating. IoT applications collect data from physical objects and send it to a central server for processing and generating commands. The security of collected data and user's commands is a top priority, and a key barrier to widespread take-up of IoT-based technologies. A cyber-attack can lead to financial losses, health hazards, and even loss of life. The proposed project will connect Cistech Ltd., an IT (Information Technology) service provider, with cybersecurity experts at the University of Waterloo to develop a machine/deep-learning (M/D-L) based security solution to detect intrusions in IoT networks for SOA applications. Together, the team will: 1) Design a software framework to evaluate different M/D-L models that use state-of-the-art optimization techniques for automated feature selection IoT device and network data; 2) Design a model selector based on hyper-parameter tuning of different M/D-L models and multiple selection criteria. Since the accuracy and false alarm rates of classification models are sensitive to the values of hyper-parameters, selection methods will be designed with multiple objectives: increasing accuracy and minimizing false alarm rate. A technique will be designed to select the best model using hyper-parameter tuning of different ensemble algorithms. The selection technique will use multiple classification evaluation metrics: detection rate, accuracy, F-score, and false alarm rate; 3) Design a proof-of-concept intrusion detection system for IoT-based SOA applications. A variety of attacks will be simulated, and the selected model from objective 2 will be used to demonstrate the efficacy of the proposed intrusion detection system. Canada has a strong IT ecosystem covering all aspects of IoT devices, including hardware, sensors, communication, security, and M/D-L techniques. Results from this project will expand the envelope of security technologies for IoT-based applications in Canada.