Enhancing Smart Home Threat Detection with Artificial Intelligence

Jaime Ibarra*, Usman Javed Butt, Ahmed Bouridane, Neil Eliot, Hamid Jahankhani

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageEnglish
Title of host publicationCybersecurity, Privacy and Freedom Protection in the Connected World
Subtitle of host publicationProceedings of the 13th International Conference on Global Security, Safety and Sustainability, London, January 2021
EditorsHamid Jahankhani, Arshad Jamal, Shaun Lawson
Place of PublicationCham
Number of pages19
ISBN (Electronic)9783030685348
ISBN (Print)9783030685331, 9783030685362
Publication statusPublished - 18 Jun 2021
Event13th International Conference on Security, Safety and Sustainability: ICGS3-21 - Virtual, London, United Kingdom
Duration: 14 Jan 202115 Jan 2021

Publication series

NameAdvanced Sciences and Technologies for Security Applications
ISSN (Print)1613-5113
ISSN (Electronic)2363-9466


Conference13th International Conference on Security, Safety and Sustainability
Country/TerritoryUnited Kingdom


Dive into the research topics of 'Enhancing Smart Home Threat Detection with Artificial Intelligence'. Together they form a unique fingerprint.

Cite this