An Efficient Machine Learning Based Approach for Early Detection of Network Intrusion Attacks

With the rapid growth of computer networks, intrusion detection has become a critical challenge in ensuring network security. This paper proposes an efficient machine learning-based intrusion detection system using supervised learning algorithms. Publicly available benchmark datasets were utilized to train and test the proposed model. Feature selection techniques were applied to improve classification accuracy and reduce computational complexity. Experimental results demonstrate that the proposed approach achieves high detection accuracy and reduced false alarm rates compared to traditional methods. The study highlights the effectiveness of machine learning techniques in enhancing network security systems