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              <text>Sonekar, Shrikant V.; Kshirsagar, Pravin R.; Unhelkar, Bhuvan; Chakrabarti, Prasun; Maram, Balajee; Upreti, Kamal</text>
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              <text>An Algorithmic Approach to Intrusion Detection in Ad Hoc Wireless Networks Based on Artificial Intelligence</text>
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              <text>01-01-2025</text>
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              <text>2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.1357-1361</text>
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              <text>&lt;a href="https://doi.org/10.1109/IC363308.2025.10956232" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/IC363308.2025.10956232&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105003901880?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105003901880?origin=resultslist&lt;/a&gt;</text>
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              <text>Sonekar S.V., J D College of Engineering &amp;amp; Management, Department of Computer Science &amp;amp; Engineering, Nagpur, India; Kshirsagar P.R., J D College of Engineering &amp;amp; Management, Department of Electronics &amp;amp; Telecommunication Engineering, Nagpur, India; Unhelkar B., Muma College of Business, University of South Florida, Department of Infornation Systems and Decision Sciences, Sarasata, United States; Chakrabarti P., Sir Padampat Singhania University, Department of Computer Science &amp;amp; Engineering, Rajasthan, Udaipur, 3313606, India; Maram B., Sr University, Department of Computer Science and Artificial Intelligence, Telangana, Warangal, 506371, India; Upreti K., Christ University, Department of Computer Science, Delhi NCR, Ghaziabad, India</text>
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              <text>The self-configured, autonomous, and framework-free modes of communication that mobile adhoc networks (MANETs) offer have revolutionized our culture. As a result, efforts have been made to explore ways to maximize the potential of MANETs through increased and improved utilization. Standards for AI have been developed thanks to the most recent release of new machine learning technologies. Different security-related issues from malware assaults affect mobile ad hoc networks (MANETs). Any node operates as a router to move data without centralized control, making nodes more vulnerable to threats from other nodes or attackers because of their brief existence. Because of this, MANET needs particular security policies to detect the incorrect entrance of misbehaving nodes. If all nodes are self-assured and correctly collaborate, the networks function better. The paper presents a practical artificial intelligence algorithm-based security system that uses AdaBoost and DT algorithms to recognize and identify packet falling nodes, classify information packets as normal or abnormal, and detect insider threats in real-time. The results showed that DT performed better than AdaBoost, with a 98% accurate prediction rate. Consequently, DT is better able to recognize damaging attacks in MANETs.  2025 IEEE.</text>
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              <text>AdaBoost Algorithm; Artificial intelligence; Decision Tree Algorithm; MANETs; Security</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>ISBN: 979-833152749-5;</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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