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Title	A Study On Anomaly-Based Intrusion Detection Systems Employing Supervised Deep Learning Techniques
ID_Doc	5008
Authors	Shaffi A.S.; Chacko J.V.; Eliyan G.; Balaji S.
Year	2024
Published	Proceedings - 2024 8th International Conference on Inventive Systems and Control, ICISC 2024
DOI	http://dx.doi.org/10.1109/ICISC62624.2024.00069
Abstract	The rise of smart cities, driverless automobiles, smart watches, and mobile banking has led to increased reliance on the Internet. Although technology has enormous advantages for people and society, it also introduces threats. Cyber attacks are more common in this digital world and the intruders are working hard to enter into the business websites or an organization data server. Hence, integrating an intrusion detection system (IDS) is essential in the security environment because it enables IT infrastructure to resist threats. Conventional IDS are limited to detect only sophisticated attacks and fails to detect the hidden and other anomalies that occur in the network Systems. An accurate and strong approach for IDS must be created to solve this difficulty for the successful functioning of businesses. The present study explores the use of supervised Deep Learning (DL) techniques and recommends an effective model for anomaly detection. The performance evaluation of the model is performed using NSL-KDD dataset and KDDcup99 and the explored DL models in this study are compared in terms of accuracy and precision. © 2024 IEEE.
Author Keywords	Anomaly-based; Convolutional Neural Networks(CNN); Deep Learning(DL); Intrusions detection

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