Cyber Threat Detection: A Machine Learning Approach

Abstract

In today’s digital landscape, the frequency and sophistication of cyber threats pose significant challenges to organizations, rendering traditional security measures increasingly inadequate. This research study presents an integrated methodology leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the identification and mitigation of cyber threats. We outline a systematic methodology that includes the collection and preprocessing of relevant datasets, the development of predictive models using various ML algorithms, and the rigorous evaluation of model performance through established metrics. This research highlights the important benefits of using AI and ML in cybersecurity systems, paving the way for more proactive and adaptive threat response strategies. This research introduces a solid approach for continuous improvement and real-time threat detection. It contributes to the growing understanding of how to strengthen organizations’ defenses against cyber threats. This approach uses several machine learning models, including Decision Trees, Random Forests, Logistic Regression, Support Vector Machines, Naive Bayes, K-Nearest Neighbors, and Neural Networks, to show how they can effectively identify and respond to different types of cyber threats. Our research has found that the Random Forest model achieved an impressive experimental accuracy of 98.52%. This study demonstrates the superior accuracy and efficiency of AI-driven approaches in identifying both known and emerging threats, paving the way for more proactive and adaptive cybersecurity strategies.