Year: 2022
Type: article
Abstract: : Internet of Things (IoT) revolution has challenged IoT security architects to great extent by exploiting the entire layered IoT architecture as attack surface for different cyber-attacks. Rather it has become easier to execute attacks due to non-standardized security architectures of IoT technologies. This study reviews the possibilities of attack surfaces available in IoT ecosystem and techniques used for early detection of malware or attacks. There are a number of attacks in which an IoT device is used as an attack surface for attacking some other system resource including attack vectors such as backdoor, password attacks, cross site scripting, ransomware, DDos, SQL injection, scanning, spying which can infect the IoT system as well as other paired devices through it. This work studies the possible attack types through IoT ecosystem and exploiting machine learning techniques in detection of attacks well in time. A set of machine learning algorithm from each family of machine learning is evaluated for one of the open-source data sets and their performance is compared for seven different IoT device types and eight types of attacks on each of the devices. The performance metrics used for evaluation of algorithms are recall, precision, F-score and accuracy. The study also presents issues related to the variation in performance of machine learning algorithms based on the composition of attributes of different types.
Authors: GEETANJALI SURANGE, PALLAVI KHATRI
Institution: ITM University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4298201225