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Machine Learning for Intrusion Detection by emmanuel.oguchi2024 is a document available to read on EtoBox.

This study explores the enhancement of network security through the integration of machine learning and deep learning techniques in intrusion detection systems (IDS). It evaluates various models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM), highlighting their effectiveness in identifying and preventing network intrusions. The research emphasizes the necessity for adaptive cybersecurity frameworks and innovative data fusion methods to address the evolv

Author

emmanuel.oguchi2024

Language

EN