Open AccessDetections of IoT Attacks via Machine Learning-Based Approaches with Cooja
Author(s) -
Ali Hamid Farea,
Kerem Küçük
Publication year - 2022
Publication title -
eai endorsed transactions on internet of things
Language(s) - English
Resource type - Journals
ISSN - 2414-1399
DOI - 10.4108/eetiot.v7i28.324
Subject(s) - computer science , decision tree , random forest , internet of things , machine learning , tree (set theory) , artificial intelligence , 6lowpan , data mining , computer security , ipv6 , the internet , world wide web , mathematical analysis , mathematics
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.