Cybersecurity Attack Detection using Gradient Boosting Classifier

In the rapidly evolving digital landscape, cybersecurity attacks have become increasingly sophisticated, posing monumental threats to organi- zations and individuals alike. Among the myriad of cyber threats, our focus in this paper is on detecting anomalies indicative of potential cy- ber attacks, specifically targeting network traffic. Detecting these attacks promptly and accurately is not just a technical challenge but a necessity to ensure data integrity, user trust, and operational continuity. This paper presents a comprehensive approach to detect such cybersecurity anoma- lies using the Gradient Boosting Classifier, a machine learning algorithm renowned for its predictive prowess. Our proposed solution encompasses advanced data preprocessing techniques, meticulous feature engineering, and rigorous model evaluation metrics. The applications of such a detec- tion system are vast, spanning across sectors like finance, healthcare, and e-commerce, acting as a bulwark against data breaches and unauthorized intrusions. The paper outlines our methodology, from data acquisition and preprocessing to modeling and evaluation, providing a blueprint for effective cyber attack detection.