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Network Intrusion Detection System
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This work focuses on Intrusion Detection Systems (IDS), which are essential for protecting computer networks from cyberattacks and unauthorized access. With the increasing complexity of modern network traffic, detecting threats has become more challenging. The study highlights the importance of high-quality datasets and effective evaluation metrics in developing reliable IDS models.It examines the limitations of traditional datasets like KDD Cup 99 and discusses modern alternatives such as UNSW-NB15, CICIDS2017, and TON-IoT that better represent real-world attack scenarios. The work also emphasizes key evaluation metrics, including accuracy, precision, recall, and F1-score, which help measure detection performance and reduce false alarms. Overall, it provides insights into improving IDS using machine learning techniques for better security in evolving network environments.
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