Machine learning-based approaches for intrusion detection

Abstract

The rapid increase in network security threats necessitates the development of effec- tive intrusion detection model capable of identifying and mitigating malicious activities. This approach presents the development and evaluation of an intrusion detection model utilizing various machine learning algorithms that provide a potential pathway for at- tacks detection. The model is designed to detect and mitigate malicious activities within a network environment. To train and test the machine learning models, a comprehensive dataset sourced from Kaggle is employed, encompassing a wide range of attack scenarios and normal network behaviors. The models are evaluated using classificatio nevaluation metrics such as accuracy, precision, and recall, demonstrating their capability to accu- rately classify instances and identify potential threats. The primary objective of this approach is to enhance the detection of attacks irrespective of their types, while minimiz- ing the occurrence of false positives. The results obtained highlight the effectivenes sof the intrusion detection model in providing a proactive network security solution, enabling timely detection and response to malicious activities. The finding so fthi sprojec tcon- tribute to the fiel do fintrusio ndetection ,pavin gth ewa yfo rimprove dsecurit ymeasures in network environments.