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Intrusion Detection in IoT Networks Using Deep Learning Algorithm

Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
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Information 2020, 11(5), 279; https://doi.org/10.3390/info11050279
Received: 26 April 2020 / Revised: 14 May 2020 / Accepted: 14 May 2020 / Published: 21 May 2020
(This article belongs to the Section Artificial Intelligence)
The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible. View Full-Text
Keywords: machine learning; deep learning; Internet of Things; distributed denial-of-service attack; intrusion detection machine learning; deep learning; Internet of Things; distributed denial-of-service attack; intrusion detection
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Susilo, B.; Sari, R.F. Intrusion Detection in IoT Networks Using Deep Learning Algorithm. Information 2020, 11, 279.

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