# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
###### Abstract  
As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution. Internet of Things; wireless sensor networks; security; machine learning; blockchain; detection; prevention; cyberattacks; integration; review +
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significantly decreased, no human intervention is required, and they perform better in dynamic environments. On the other hand, BC allows highly secure data transactions within any network similar to WSNs [9]. The fact that ML and BC can potentially provide promising solutions and effective mechanisms to protect and secure WSNs against cyberattacks has motivated several recent research works focused on evaluating the performance of BC and ML to secure WSNs. The performance of ML and BC is affected by the challenging characteristics of WSNs, such as its large generated data volume, which are extremely hard to manage, especially when considering highly dense networks. To this end, this paper attempts to answer the following overarching research questions. How is ML used to detect WSN cyberattacks? How is BC used to prevent WSN cyberattacks? How can the integration of ML and BC provide an effective framework to protect and secure WSNs against cyberattacks? Finally, What are the key technical challenges related to this integration? Thus, the main contributions of this survey are: (a) classification of WSN cyberattacks and the unique characteristics that complicate the design of effective detection and prevention mechanisms against cyberattacks; (b) a literature review of the existing Intrusion Detection System (IDS) architectures in the context of WSNs; (c) a comprehensive taxonomy of ML and BC along with an evaluation of relevant existing security techniques and challenges; (d) discussion of an integrated solution incorporating both technologies towards development of a WSN that is significantly immune against attacks; and (e) an ultimate overview of our approach to providing a lightweight and integrated ML and BC framework towards enhanced protection against cyberattacks in WSN contexts.