Data-driven Intelligent Attack Detection in Multilateral, Large-Scale and Heterogeneous Internet of Things Environments

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The main idea of the Internet of Things (IoT) is to enable devices (e.g., smart vehicles, home gadgets, etc.) to sense the environment, collect pertinent data, analyse these data and make some actions based on the extracted insights. Such devices often possess low processing capabilities and limited memory and storage capacity. Cloud computing has always been the main backbone that IoT devices rely on to accommodate their storage and analytical needs. However, the fact that cloud servers are often deployed in locations that are quite far from the IoT devices and the emergence of delay-critical IoT applications urged the need for extending the cloud architecture to support delay-critical services. In this context, the concept of fog computing has been proposed to provide data analytics and decision-making closer to the IoT devices. The above-described IoT multi-player architecture is likely to be faced with non-conventional security challenges due to the multi-lateral, heterogeneous and large-scale nature of this environment which consists of billions of IoT, fog and cloud devices of different types and sizes. Our long-term goal of this research program is to improve the efficiency of detecting advanced attack patterns in the IoT-fog-cloud environments. The novelty of this research program lies in studying the security of the IoT in a comprehensive multilateral environment wherein the IoT, fog and cloud layers coexist and interact, thus considering new non-conventional vulnerabilities and threats. The proposed research program spans over three main research tracks, which are: (1) big data analytics for attack recognition in which deep and ensemble learning approaches will be designed and implemented to recognize advanced attack patterns; (2) multi-sided trust establishment in which multi-sided (i.e., IoT-to-fog, fog-to-IoT, IoT-to-cloud, cloud-to-IoT, fog-to-cloud and cloud-to-fog) trust establishment solutions will be investigated to improve the security of the communication channels among these different coexisting parties; and (3) data-driven cyber-security decision-making in which data-driven mathematical models that capitalize on the insights extracted from the data analytics and trust tracks will be designed to help security administrators effectively counter attackers' wily strategies and efficiently deal with the resource and budget limitation problems. The proposed research program is anticipated to have positive impacts at the economic, social and academic levels through (a) protecting the massive investments that are being made by the public and private sectors in Canada in the IoT technology by offering advanced security solutions for the IoT assets, (b) protecting individuals' data as they flow through the different IoT layers (i.e., IoT, fog and cloud), and (c) offering high-quality training to students at the PhD, Master's and undergraduate levels in such a way to prepare them for the Canadian job market's needs and standards.