AI/ML Models for Mitigating False Positives in Large-Scale Security Alert Systems

Abstract

The proliferation of security alert systems in large-scale enterprise environments has underscored the critical need for mitigating false positives, a persistent challenge that undermines the efficiency and efficacy of Security Operations Centers (SOCs). This paper provides a comprehensive exploration of artificial intelligence (AI) and machine learning (ML) methodologies to address this challenge. Specifically, it examines the application of supervised machine learning models, including ensemble learning algorithms such as Random Forests, Gradient Boosting Machines (GBMs), and XGBoost, alongside deep neural networks (DNNs), to improve the accuracy of threat detection and reduce false positive rates in high-volume security alert systems. The study leverages insights from practical implementations within SOC operations, particularly through tools such as Datadog and Chronicle Security AI.

The research begins by delineating the nature and scale of the false positive problem in modern security infrastructures, emphasizing its detrimental impact on resource allocation, analyst fatigue, and response prioritization. Subsequently, it delves into the theoretical underpinnings and practical considerations of supervised learning models tailored to classify and filter alerts with higher precision. Ensemble learning methods are highlighted for their ability to combine multiple weak learners to form robust predictive models, while DNNs are explored for their capacity to learn intricate patterns and correlations within multidimensional alert data.

A critical analysis of dataset preprocessing techniques, including feature engineering, dimensionality reduction, and class imbalance management, is provided to contextualize the optimal training of ML models. The integration of advanced techniques, such as synthetic minority oversampling (SMOTE) for handling imbalanced datasets, and feature importance metrics for interpretability, is discussed in detail. Furthermore, the paper presents case studies illustrating the deployment of Datadog and Chronicle Security AI in SOC operations, showcasing how these platforms utilize AI/ML to filter, prioritize, and escalate alerts effectively. Practical examples demonstrate the tangible reduction of false positive rates while maintaining high true positive rates, underscoring the models' utility in real-world scenarios.

The evaluation metrics employed include precision, recall, F1-score, and Receiver Operating Characteristic (ROC) curve analysis, providing a robust framework to measure the effectiveness of the proposed solutions. Comparative analysis with traditional rule-based systems highlights the superiority of AI/ML models in adapting to evolving threat landscapes. The paper also examines the limitations and challenges of deploying such models, including computational overhead, data privacy concerns, and adversarial attacks designed to exploit ML vulnerabilities. Strategies to mitigate these challenges, such as model retraining, adversarial robustness techniques, and the adoption of privacy-preserving federated learning, are proposed.