Anomaly-based Intrusion Detection Techniques in Internet of Things Ecosystem: A Review

Abstract

With a vast array of smart and connected devices and applications available in
many areas, including green IoT-based agriculture, smart farming, smart homes,
smart transportation, smart health, smart grid, smart cities, and smart
environment, the Internet of Things (IoT) technology has emerged to enhance
people's lives. IoT devices are susceptible to cyberattacks. Though, researchers
have sufficiently embraced the use of diverse techniques and algorithms as a means
of securing data and information generated and transmitted in the Internet of
Things ecosystem. Additionally, these techniques have been effectively applied in
a number of domains, demonstrating its superiority in tackling intrusion detection
attacks. The anomaly-based Intrusion Detection System (IDS) has an edge in
identifying zero-day attacks because signature-based detection is limited when it
comes to unknown threats. Therefore, this paper explicitly and systematically
analyzed current techniques deployed in IoT ecosystem for the detection of
anomaly-based intrusion attacks. Also, the processes and functionalities adopted
by the techniques to predict the abnormality-based intrusion attacks, development
and simulation tools adopted to implement and evaluate the effectiveness and
performance of the techniques are highlight and discussed extensively. Finally, a
summary of challenges and weaknesses of the techniques are briefly discussed, for
onward investigation in future researches.