The SOC Alert Analyzer and Correlator is an advanced cybersecurity platform designed to help Security Operations Center (SOC) teams reduce alert fatigue, minimize false positives, and improve incident response efficiency. Modern SOC analysts often face thousands of security alerts daily from SIEM platforms, firewalls, EDR solutions, IDS/IPS systems, cloud environments, and threat intelligence feeds. This project addresses that challenge by automatically analyzing, correlating, and prioritizing security events to identify genuine threats faster.

The platform ingests logs from multiple sources, including Splunk, Elastic Stack (ELK), Wazuh, Graylog, Windows Event Logs, Syslog, firewall logs, cloud logs, and endpoint security tools. A powerful correlation engine links related events based on users, assets, applications, timestamps, and IP addresses, transforming multiple low-level alerts into actionable security incidents.

To improve threat visibility, the solution maps security events to the MITRE ATT&CK framework, helping analysts understand attacker behavior, tactics, and techniques. A baseline behavior engine continuously monitors normal user and system activities, enabling the detection of anomalies such as unusual login times, abnormal network traffic patterns, suspicious file access, and unauthorized application usage.

The platform leverages machine learning algorithms such as Random Forest, XGBoost, Isolation Forest, and One-Class SVM to classify alerts as true positives, false positives, or suspicious events. This significantly reduces analyst workload and allows security teams to focus on high-priority threats. A dynamic risk-scoring engine evaluates factors such as asset criticality, malicious IP reputation, user privileges, historical activity, and MITRE ATT&CK mappings to assign a contextual risk score to every incident.

Real-time notifications are delivered through Slack, Telegram, and email integrations, ensuring immediate visibility into critical security events. The dashboard provides security analysts with actionable insights through alert trends, MITRE ATT&CK heatmaps, attack timelines, asset risk scores, and incident tracking metrics.

Advanced capabilities include Threat Intelligence integration with sources such as AbuseIPDB, VirusTotal, and AlienVault OTX, SOAR-based automated response actions, and LLM-assisted investigations that generate incident summaries, root cause analysis, and analyst recommendations.

Built using Python, FastAPI, Elasticsearch, PostgreSQL, Pandas, NumPy, Scikit-learn, and XGBoost, this project demonstrates expertise in SOC Operations, SIEM Engineering, Threat Hunting, Cybersecurity Analytics, Security Automation, Machine Learning for Cybersecurity, MITRE ATT&CK Mapping, and Incident Response. It is an ideal project for cybersecurity professionals, SOC analysts, threat hunters, and defensive security practitioners seeking to showcase real-world security engineering capabilities.