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              <text>The Development of ID System for Detecting Attacks in WSN Through Ontology Method and its Strategy</text>
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              <text>Cyberattacks; Internet of Things (IoT); Intrusion Detection System (IDS); ontology; wireless sensor networks</text>
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              <text>Cybercriminals are becoming increasingly targeted by the rapid expansion of the Internet of Things (IoT), leading to an increase in cyberattacks targeting IoT devices and their communication channels These attacks, if failure to detect may result in significant service disruption, financial loss or damage to sensitive data. Real-time intrusion detection is essential to ensure reliability, security and profitability of IoT applications. This study introduces a new intrusion detection system designed for IoT devices that uses deep learning (DL). Utilizing ontology in wireless sensor networks (WSN), this intelligent system detects suspicious activities that pose a threat to connected IoT devices with configuration-neutral design provides ease of use, while the test performance analysis is simulated and real-world. It highlights its strong performance in determining admissions. The effectiveness of the system against many types of attacks such as denial of service, workholes, blackholes, opportunistic service attacks, etc. is confirmed by experimental research and furthermore, the system achieves F1 scores, accuracy and the number of memories. This advanced deep learning intrusion detection system shows great promise to improve IoT network security due to its high detection rate.  2024 IEEE.</text>
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              <text>Sheela M.S.; Venkateswarlu M.; Donald C.; Kadiri P.; Al-Hameed M.R.; Safi H.; Saadoun O.N.</text>
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              <text>2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 391-394.</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/ICACITE60783.2024.10616975" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICACITE60783.2024.10616975&lt;/a&gt;
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              <text>ISBN: 979-835036016-5</text>
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              <text>Online</text>
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              <text>Sheela M.S., Vel Tech Rangarajan Dr.Sagunthala R&amp;amp;d Institute of Science and Technology, Ece, Chennai, Avadi, India; Venkateswarlu M., Kampala International University, Faculty of Science and Technology, Department of Computing, Western Campus, Uganda; Donald C., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India; Kadiri P., Mohan Babu University, School of Computing, Department of Aiml, Andhra Pradesh, Tirupati, India; Al-Hameed M.R., National University of Science and Technology, College of Health and Medical Technology, Dhi Qar, 64001, Iraq; Safi H., Al-Farahidi University, Baghdad, Iraq; Saadoun O.N., Al-kunooze University College, Basra, Iraq</text>
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