Enhancing Smart Home Threat Detection with Artificial Intelligence

The chapter focuses on building a theoretical network, which supports the
protection of home networks from critical cyberattacks. A framework is proposed which aims to augment a home router with machine learning techniques to identify threats. During the current pandemic, employees have been working from home. So it is reasonable to expect that cyberattacks on households will become more common to leverage access into corporate networks. The model described in this chapter is for a single network; however, the network would be segmented into regions to avoid a wider compromise. Since the deployment of 5G, mobile threats are rising steadily. Therefore, the UK requires a robust plan to identify and mitigate all forms of threats including nation-state, terrorism, hacktivism. Additionally, the model dynamically analyses traffic to identify trends and patterns; therefore, supporting on the building of a resilient cyber defence. The emphasis in this model is to bridge the gap of trust between the government and the public, so that trust and transparency is established by a regulatory framework with security recommendations. At present, there is no authorisation to collect this data at national level, nor is there trust between the public and government regarding data and storage. It is hoped that this model would change human perception on the collection of data and contribute to a safer UK.