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            <name>Title</name>
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                <text>Conference Papers</text>
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    <name>Conference Paper</name>
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          <name>Title</name>
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              <text>Distributed DoS Detection in IoT Networks Using Intelligent Machine Learning Algorithms</text>
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          <name>Subject</name>
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              <text>DDoS; IoT; Nave Bayes; Random Forest; ZeroR</text>
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          <name>Description</name>
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              <text>The threat of a Distributed Denial of Service (DDoS) attack on web-based services and applications is grave. It only takes a few minutes for one of these attacks to cripple these services, making them unavailable to anyone. The problem has further persisted with the widespread adoption of insecure Internet of Things (IoT) devices across the Internet. In addition, many currently used rule-based detection systems are weak points for attackers. We conducted a comparative analysis of ML algorithms to detect and classify DDoS attacks in this paper. These classifiers compare Nave Bayes with J48 and Random Forest with ZeroR ML as well as other machine learning algorithms. It was found that using the PCA method, the optimal number of features could be found. ML has been implemented with the help of the WEKA tool.   2021 IEEE.</text>
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          <name>Creator</name>
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              <text>Binny S.; Pendyala S.S.; Pimo S.J.; Aurelia S.; Reddy P.R.; Satyanarayana D.S.S.</text>
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              <text>2021 2nd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2021 - Proceedings</text>
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          <name>Publisher</name>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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          <name>Date</name>
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              <text>2021-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICSTCEE54422.2021.9708569" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICSTCEE54422.2021.9708569&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125700001&amp;amp;doi=10.1109%2FICSTCEE54422.2021.9708569&amp;amp;partnerID=40&amp;amp;md5=0e808f3809502a1549e66b8ba5ca6965" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125700001&amp;amp;doi=10.1109%2fICSTCEE54422.2021.9708569&amp;amp;partnerID=40&amp;amp;md5=0e808f3809502a1549e66b8ba5ca6965&lt;/a&gt;</text>
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          <name>Rights</name>
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              <text>Restricted Access</text>
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              <text>ISBN: 978-166548297-4</text>
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          <name>Format</name>
          <description>The file format, physical medium, or dimensions of the resource</description>
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              <text>Online</text>
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          <name>Language</name>
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              <text>English</text>
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          <name>Type</name>
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              <text>Conference paper</text>
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              <text>Binny S., Kristu Jyoti College of Management and Technology, Department of Computer Science, Kerala, India; Pendyala S.S., Institute of Aeronautical Engineering, Department of ECE, Hyderabad, India; Pimo S.J., St. Xavier's Catholic College of Engineering, Nagercoil, India; Aurelia S., Christ University, Bangalore, India; Reddy P.R., Geethanjali Institute of Science and Technology, ECE, Nellore, India; Satyanarayana D.S.S., Chalmers University of Technology, Goteborg, Sweden</text>
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