EAI Endorsed Transactions on Internet of Things Research Article 1 Detections of IoT Attacks via Machine Learning-Based Approaches with Cooja Ali Hamid Farea 1,* , and Kerem Küçük 2 1 Department of Computer Engineering at Kocaeli University, Kocaeli, Turkey 2 Department of Software Engineering at Kocaeli University, Kocaeli, Turkey Abstract Once hardware becomes "intelligent", it is vulnerable to threats. Therefore, IoT ecosystems are susceptible to a variety of attacks and are considered challenging due to heterogeneity and dynamic ecosystem. In this study, we proposed a method for detecting IoT attacks that are based on ML-based approaches that release the final decision to detect IoT attacks. However, we have implemented three attacks as a sample in the IoT via Contiki OS to generate a real dataset of IoT-based features containing a mix of data from malicious nodes and normal nodes in the IoT network to be utilized in the ML-based models. As a result, the multiclass random decision forest ML-based model achieved 98.9% overall accuracy in detecting IoT attacks for the real novel dataset compared to the decision tree jungle, decision forest tree regression, and boosted decision tree regression, which achieved 87.7%, 93.2%, and 87.1%, respectively. Thus, the decision tree-based approach efficiently manipulates and analyzes the KoÜ-6LoWPAN-IoT dataset, generated via the Cooja simulator, to detect inconsistent behavior and classify malicious activities. Keywords: IoT security, Attacks, Machine Learning-based approaches, Decision tree-based models, Cooja simulator. Received on 01 March 2022, accepted on 02 April 2022, published on 07 April 2022 Copyright © 2022 Ali Hamid Farea et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited. doi: 10.4108/eetiot.v7i28.324 1. Introduction An Internet of Things (IoT) is a network of physical objects containing sensors, actuators, microcontrollers, and smart appliances that gather and transfer information and interact with their surroundings [1], [2], allowing these devices to generate and exchange data with minimal human intervention. It is one of the most promising technologies and the world is already beginning to utilize various IoT technologies. It communicates with each other via various protocols [3] as well as interacts with a wide range of applications, including smart cities, building automation, safety, surveillance systems, logistics, healthcare, economy, calamity and agriculture [4], [5], [3]. Therefore, it offers a large number of attractive qualities that have made us rely on it in our daily applications with best-effort and real-time [6], [7]. * Corresponding author. Email: 195112025@kocaeli.edu.tr The IoT cloud provides capabilities for collecting, processing, managing, and storing massive amounts of data in real-time [8], [9]. This data may be easily accessed remotely via industries, governments, monitoring tools, and related services, allowing them to make decisions as needed [10], [11]. It is essentially a powerful, high-performance network of servers designed to do high-speed data processing for billions of connected devices [12]. IoT technologies have certain properties in common that are described as heterogeneity, auto-configuring, dynamic ecosystem, smart, large scale, and connectivity [4], [13], [14], [15]. For example, the IoT ecosystem includes extremely different technologies and protocols, adaptive protocols, a variety of factors that may be influenced in order to adapt to environmental changes, etc. These components (large scale) work together in a cooperative and smart way to share their collected data and services [16]. In many cases, the connected devices are required to offer secure and reliable services to an applicant [17]. EAI Endorsed Transactions on Internet of Things 04 2022 - 04 2022 | Volume 7 | Issue 28 | e1