![]() ![]() The dataset ISOT-CID network traffic part uses for the training ML model. ![]() This research’s significant challenges are the extracted features used to train the ML model about various attacks to distinguish whether it is an anomaly or regular traffic. This detection model uses a dataset constructed from malicious and normal traffic. This research proposes a detection framework with an ML model for feeding IDS to detect network traffic anomalies. The core of ML models’ detection efficiency relies on the dataset’s quality to train the model. Recently, machine learning (ML) is a widespread technique offered to feed the Intrusion Detection System (IDS) to detect malicious network traffic. Several tools are designed for this purpose, such as mapping networks and vulnerabilities scanning. They pass new attacks and trends these attacks target every open port available on the network. Computer networks target several kinds of attacks every hour and day they evolved to make significant risks. ![]()
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