  Security and privacy of the users have become significant concerns due to the
involvement of the Internet of things (IoT) devices in numerous applications.
Cyber threats are growing at an explosive pace making the existing security and
privacy measures inadequate. Hence, everyone on the Internet is a product for
hackers. Consequently, Machine Learning (ML) algorithms are used to produce
accurate outputs from large complex databases, where the generated outputs can
be used to predict and detect vulnerabilities in IoT-based systems.
Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT
applications to solve security and privacy issues. Several studies have been
conducted on either ML algorithms or BC techniques. However, these studies
target either security or privacy issues using ML algorithms or BC techniques,
thus posing a need for a combined survey on efforts made in recent years
addressing both security and privacy issues using ML algorithms and BC
techniques. In this paper, we provide a summary of research efforts made in the
past few years, starting from 2008 to 2019, addressing security and privacy
issues using ML algorithms and BCtechniques in the IoT domain. First, we
discuss and categorize various security and privacy threats reported in the
past twelve years in the IoT domain. Then, we classify the literature on
security and privacy efforts based on ML algorithms and BC techniques in the
IoT domain. Finally, we identify and illuminate several challenges and future
research directions in using ML algorithms and BC techniques to address
security and privacy issues in the IoT domain.
