Cybersecurity Risk Assessment using Machine Learning and Data Science Techniques

Abstract

Conventional approaches to cybersecurity risk assessment are beset by limitations when it comes to providing timely, precise, and adaptable responses to the ever-increasing variety and velocity of cyber-attacks. In order to improve threat detection, vulnerability analysis, and risk mitigation, this project investigates how cybersecurity risk assessment might be enhanced by integrating data science and machine learning (ML) techniques. The objective of this project is to create a prediction model that can detect anomalies and possible dangers in digital systems by using supervised and unsupervised learning techniques with modern data analytics. To improve the precision of threat prediction, strategies such decision trees, support vector machines, deep learning models, and anomaly detection and classification are utilized. Additionally, the study delves into the function of real-time monitoring and big data analytics as they pertain to proactive risk management. The effectiveness of the suggested approach in decreasing false positives and identifying new risks is demonstrated through evaluation using real-world cybersecurity datasets. Organizations may now assess risks and make decisions based on data thanks to the results, which contribute to intelligent cybersecurity frameworks.