Machine Learning-Based Alert Correlation for Enhanced Cybersecurity: A Multi-Classification Approach
Mouhamadou Lamine Diakhame; Chérif Diallo; Mohamed Mejri
Informations
- Revue / Journal
- 2025 9th Cyber Security in Networking Conference (CSNet)
- Éditeur
- IEEE
- ISBN
- 979-8-3315-7556-4
Résumé
Modern Security Operations Centers (SOCs) struggle with an overwhelming influx of security alerts from diverse sources, leading to alert fatigue and delayed incident response times. Conventional Security Information and Event Management (SIEM) systems typically process alerts in isolation, missing complex attack patterns that unfold across multiple detection points. This research introduces a comprehensive machine learning framework for intelligent alert correlation using three classification approaches: binary correlation detection, correlation type classification, and incident classification. We evaluated eight machine learning algorithms—Logistic Regression, Random Forest, XGBoost, LightGBM, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), CatBoost, and Support Vector Machines (SVM)—using 50,000 realistic security alerts generated from 2.30 million network flows in the CIC-IDS-2017 dataset. Our multi-classification framework incorporates temporal relationships, network topology, attack progression patterns, and severity indicators to enhance correlation precision. The experimental results reveal remarkable performance across all classification tasks. CatBoost achieved the highest overall performance with 99.27% average accuracy across all tasks, closely followed by Random Forest (99.17%) and LightGBM (99.06%). For multiclass correlation type classification, six out of eight models achieved perfect 100% accuracy, with only XGBoost and SVM showing decreases to 96.30%. Binary correlation detection demonstrated exceptional performance with three models (Random Forest, LightGBM, CatBoost) achieving 99.96% accuracy. The proposed framework demonstrates exceptional capability in reducing alert noise while maintaining optimal detection rates, significantly enhancing SOC operational efficiency and enabling more targeted threat response strategies.