Advancements in Machine Learning for Anomaly Detection in Cyber Security

The growth of complex cyber threats has spurred the investigation and development of creative approaches in anomaly detection within the area of cybersecurity. Machine learning has become a crucial technique in strengthening digital defenses against changing cyber threats due to its capacity to identify patterns and abnormalities in large datasets. This study digs into the improvements in machine learning algorithms geared particularly for anomaly identification in cybersecurity applications. Anomaly detection strategies span a broad range of methodologies, including both classic statistical approaches and more complex deep learning models. This study investigates the development of machine learning methods, emphasizing their advantages, constraints, and uses in identifying abnormal behaviors in intricate network settings. These models are highly effective in capturing complex patterns and subtle details found in cybersecurity datasets, allowing for the detection of previously unidentified risks and abnormalities with improved accuracy. In addition, the use of ensemble learning methods, such as random forests and gradient boosting machines, has enhanced the strength and scalability of anomaly detection systems. This work highlights a comprehensive analysis of various machine learning methods and anomaly detection algorithms in cybersecurity applications. It reveals that random forests achieve the highest detection accuracy at 95.2%, closely followed by gradient boosting at 94.8%. Moreover, random forests and neural networks exhibit the most effective performance in reducing false alarms, with false positive rates of 2.1% and 2.9% respectively. In terms of computing efficiency, random forests demonstrate the shortest processing time at 15.7 milliseconds, followed by neural networks at 17.9 milliseconds. While random forests and neural networks prove highly scalable, with excellent real-time performance and resilience to adversarial attacks, other models such as support vector machines and K-nearest neighbors exhibit varying levels of performance across these metrics. These insights highlight the importance of selecting appropriate algorithms based on the specific requirements and characteristics of cybersecurity datasets to ensure robust anomaly detection systems.