ABSTRACT
With the rising recurrence and refinement of digital dangers, there is a basic requirement for vigorous and proactive online protection measures. This study investigates the mix of AI methods for anticipating and identifying digital hacking breaks. Utilizing assorted datasets incorporating network logs, client ways of behaving, and framework exercises, we utilize administered learning calculations for break expectation and unaided strategies for inconsistency identification. Social examination and ongoing observing frameworks are integrated to upgrade the accuracy and practicality of danger distinguishing proof. This abstract introduces the application of the Gradient Boosting Algorithm (GBA) for malware detection. The growing complexity and diversity of malware pose significant challenges to traditional detection methods. In response, this study explores the effectiveness of GBA, a powerful machine learning technique, in identifying and classifying malware samples. By leveraging ensemble learning and iterative optimization, GBA enhances the detection accuracy by combining multiple weak classifiers into a robust model. The research demonstrates the superior performance of GBA compared to conventional approaches, showcasing its ability to effectively discern between malicious and benign software with high precision and recall rates. Through experimentation and evaluation on real-world datasets, this study elucidates the potential of GBA as a promising tool for bolstering cybersecurity defenses against evolving malware threats.
