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    <name>Conference Paper</name>
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              <text>Impact of Machine Learning Algorithms in Intrusion Detection Systems for Internet of Things</text>
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              <text>Internet of Things; Intrusion Detection System; Machine Learning</text>
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              <text>The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed.   2021 IEEE.</text>
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              <text>Jose J.; Jose D.V.; Rao K.S.; Janz J.</text>
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              <text>10th International Conference on Advances in Computing and Communications, ICACC 2021</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICACC-202152719.2021.9708404" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICACC-202152719.2021.9708404&lt;/a&gt;
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              <text>ISBN: 978-166543919-0</text>
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              <text>Online</text>
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              <text>Jose J., CHRIST University, Department of Computer Science, Bangalore, India; Jose D.V., CHRIST University, Department of Computer Science, Bangalore, India; Rao K.S., CHRIST University, Department of Computer Science, Bangalore, India; Janz J., CHRIST University, Department of Computer Science, Bangalore, India</text>
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