Machine learning and the Internet of Things security: Solutions and open challenges

Umer Farooq, Noshina Tariq, Muhammad Asim, Thar Baker, Ahmed Al-Shamma'a

Research output: Contribution to journalArticlepeer-review


Internet of Things (IoT) is a pervasively-used technology for the last few years. IoT technologies are also responsible for intensifying various everyday smart applications improving the standard of living. However, the inter-crossing of IoT systems and the multi-directional elements responsible for these systems' placement have raised new safety concerns. They generate and share a massive amount of sensitive data. Unfortunately, both the data and the devices are susceptible to many privacy and security challenges. Much research has been done to secure these infrastructures; however, Machine Learning (ML), among others, provides higher accuracy. This survey covers the major security issues and open challenges encountered by IoT infrastructures. It also encompasses an in-depth study and analysis of ML-based state-of-the-art solutions used in securing such domains. The security challenges and requirements in IoT-based systems have been highlighted, along with a discussion on how ML supports security measures in the said domain. Furthermore, the challenges associated with ML-based security solutions have been identified concerning IoT. An analysis of prevailing ML security techniques' constraints is also contemplated.
Original languageEnglish
Pages (from-to)89-104
Number of pages16
JournalJournal of Parallel and Distributed Computing
Publication statusPublished - 29 Jan 2022


  • Internet of Things
  • Machine learning
  • Security


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