Framework for Early Cyber Attack Detection Using ML Models Deployed On Fog Devices

The growing Internet of Things (IoT) is set to link facets of life including intelligent structures, residences, and whole urban areas. A key factor propelling this growth originates from progress, in the semiconductor sector. IoT promises innovative services demanding extensive data centers and real-time processing capabilities. However, an advancement in technology also promises an advancement in the attack surface and the threats these devices will have to face. IoT security is an up-and-coming field, raising awareness of the need for security mechanisms for these constrained devices. Existing solutions are computationally heavy for a traditional constrained device in the Fog. With the limitless implementations of these devices, there is a need for a security solution that can be scaled to a large majority of IoT applications. This paper presents a Python-based Framework with a variety of security methods and functions aimed at the early detection of Cyber Attacks using multiple ML models in real-time. The Framework is tested on a Raspberry Pi 4 device deployed within the fog computation layer, functioning as an intermediary between edge and cloud layers. This fog device is subsequently linked to a network of IoT devices. A distinctive aspect of this Framework is its dual perspective, catering to both developers and general users.