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              <text>Detection of DoS Attacks Using Machine Learning Based Intrusion Detection System</text>
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              <text>DoS Attacks; Intrusion Detection System; Machine learning; Support Vector Machine (SVM)</text>
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              <text>Conventional intrusion detection systems are not always sufficient due to the increasing sophistication and frequency of Denial-of-Service (DoS) attacks. This work presents a novel solution to this problem by leveraging machine learning techniques to increase the precision and efficacy of real-time intrusion detection. The system keeps a careful eye on network traffic patterns, looking for any irregularities that would point to a denial-of-service attack. An Intrusion Detection System (IDS) that utilizes machine learning technologies - specifically, neural networks and support vector machines - allows for real-time adaptation to new attack patterns. A combination of rigorous simulations and real-world testing provides empirical support for the IDS's quick detection and mitigation of DoS threats. This initiative makes a major contribution to the development of cybersecurity defenses.   2024 IEEE.</text>
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              <text>Sabu I.R.; Saju S.; Mary Anita E.A.; Sowmya T.</text>
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              <text>Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications</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/InC460750.2024.10649128" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/InC460750.2024.10649128&lt;/a&gt;
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              <text>ISBN: 979-835038365-2</text>
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              <text>Sabu I.R., CHRIST (Deemed to be University), Computer Science and Engineering, Bangalore, India; Saju S., CHRIST (Deemed to be University), Computer Science and Engineering, Bangalore, India; Mary Anita E.A., CHRIST (Deemed to be University), Computer Science and Engineering, Bangalore, India; Sowmya T., CHRIST (Deemed to be University), Computer Science and Engineering, Bangalore, India</text>
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