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Comparative Analysis of Machine Learning Techniques for IoT Anomaly Detection Using the NSL-KDD Dataset

International journal of computer science and network security : IJCSNS, v.23 no.1, 2023년, pp.46 - 52  

Zaryn, Good (Department of Mathematical and Computer Sciences, Indiana University of PA) ,  Waleed, Farag (Department of Mathematical and Computer Sciences, Indiana University of PA) ,  Xin-Wen, Wu (Department of Computer Science, University of Mary Washington) ,  Soundararajan, Ezekiel (Department of Mathematical and Computer Sciences, Indiana University of PA) ,  Maria, Balega (Department of Mathematical and Computer Sciences, Indiana University of PA) ,  Franklin, May (Department of Mathematical and Computer Sciences, Indiana University of PA) ,  Alicia, Deak (Department of Mathematical and Computer Sciences, Indiana University of PA)

Abstract AI-Helper 아이콘AI-Helper

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algo...

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표/그림 (7)

참고문헌 (17)

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