Efficient Threat Detection Machine Learning Techniques for Intrusion Detection Utilizing Data Engineering

This study presents a novel methodology for enhancing intrusion detection systems (IDS) through the integration of state-of-the-art data engineering techniques and machine learning approaches. Traditional intrusion detection systems are facing increasing challenges in keeping pace with the continuously evolving complexity of cyber-attacks. By means of rigorous data preparation, feature extraction, and model modification, The proposed work augments the scalability and precision of threat detection methodologies. Through the integration of machine learning and data engineering, the methodology presents a potential resolution to the obstacle of implementing effective threat detection in the realm of network security. By means of comprehensive testing and evaluation, this showcases substantial enhancements in the accuracy of detection and the overall performance of the system. Considering the critical necessity for proactive measures in countering perpetually evolving cyber threats, this research contributes significantly to the growing body of knowledge regarding cybersecurity defenses. The SVM achieves accuracy of 95%.