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Machine Learning for anomaly detection in IoT networks : Malware analysis on the IoT-23 data set

Stoian, N.A. (2020) Machine Learning for anomaly detection in IoT networks : Malware analysis on the IoT-23 data set.

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Abstract:The Internet of Things is one of the newer developments in the domain of the Internet. It is defined as a network of connected devices and sensors, both physical and digital, that generate and exchange large amounts of data without the need for human intervention. As a result of eliminating the need for human operators, the IoT (Internet of Things) can process more data than ever before faster and more efficient. This paper focuses on the security aspect of IoT networks by investigating the usability of machine learning algorithms in the detection of anomalies found within the data of such networks. It examines ML algorithms that are successfully utilized in relatively similar situations and compares using a number of parameters and methods. This paper implements the following algorithms: Random Forest (RF), Naive Bayes (NB), Multi Layer Perceptron (MLP), a variant of the Artificial Neural Network class of algorithms, Support Vector Machine (SVM) and AdaBoost (ADA). The best results were achieved by the Random Forest algorithm, with a accuracy or 99.5%.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Business & IT BSc (56066)
Link to this item:http://purl.utwente.nl/essays/81979
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