University of Twente Student Theses
Anomaly detection in IoT : federated learning approach on the IoT-23 Dataset.
Merchan, M. (2022) Anomaly detection in IoT : federated learning approach on the IoT-23 Dataset.
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| Abstract: | The use of smart devices with software, sensors and other technologies(IoT) has become more common in the last few years. They aim to be interconnected through a network and exchange data with the purpose of reducing human intervention over specific tasks. This increase in IoT devices has incremented the data transit over a network. These devices' interactions and structure have raised the problem of malware attacks, especially over these devices, which can be manipulated to replicate more attacks. Because of this, there is a concern for identifying potential attacks. AI appears as a solution to this problem, and the Federated Learning approach was used as a technique of Machine Learning and using the IoT23 as Data-set to train and test the model. A Multi-layer Perceptron was used for both central server and federated members and Federated Averaging as an aggregation technique. The results showed that Federated Learning has the potential as a decentralized technique, which means that it benefice each device in its inner ML model. The model scored 100\% accuracy on some devices. However, the general learning was affected by this decentralized method having at most 70\% of accuracy. The model's performance was affected because of the data, distribution and quantity of each member's dataset. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/91771 |
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