ThreatDetector: An Intelligent SOC Phishing Detection and Automated Response System Based on Machine Learning

ThreatDetector is a Security Operations Center (SOC) dashboard integrating machine learning, threat intelligence, and automated response capabilities, designed to address enterprise security threats posed by phishing attacks. It provides enterprises with a proactive defense solution and improves SOC operational efficiency by intelligently detecting phishing and malicious URLs, performing threat intelligence checks, and automatically generating security responses.

Phishing attacks have evolved from crude scam emails to highly personalized and hard-to-identify sophisticated attacks. Over 90% of cyberattacks start with phishing emails, and enterprises take an average of 280 days to detect data breaches. Traditional defense relies on blacklists and signature matching, which has problems such as inability to identify zero-day attacks, easy bypassing, and high false positive rates. Manual processing of massive alerts is inefficient and error-prone.

ThreatDetector builds a three-layer defense system: 1. Machine learning-driven URL detection: Learns malicious patterns from multiple dimensions such as URL structure and domain information to identify obfuscated short links and counterfeit domains; 2. Real-time threat intelligence integration: Queries authoritative sources like VirusTotal and URLhaus to fill model blind spots; 3. Automated response: Performs actions like URL blocking, firewall rule deployment, and terminal isolation in seconds to shorten response time.

1. Data collection and preprocessing: Collects URLs from channels like email gateways and web proxies, cleans and standardizes them, then extracts features; 2. Model training: Uses ensemble learning methods (Random Forest + Gradient Boosting Tree), optimizes via cross-validation to balance accuracy and recall; 3. Dashboard visualization: Provides real-time detection statistics, threat trend analysis, and geographic heatmaps to help analysts quickly grasp the security situation.

Applicable to: 1. Enterprise email security: Integrates with email gateways to isolate phishing emails or add warning labels; 2. Web security gateway: Analyzes accessed URLs in real time, blocks malicious websites, and records events; 3. SOAR integration: Acts as a detection engine to trigger response playbooks and collaborate with existing security facilities.

Limitations: The model needs continuous updates to deal with new types of attacks; threat intelligence relying on external APIs may be affected by network issues or rate limits. Improvement directions: Introduce deep learning to enhance variant recognition capabilities, support QR code phishing detection, and develop differentiated response strategies based on risk levels.

ThreatDetector demonstrates the potential of machine learning in cybersecurity. Through the organic combination of intelligent detection, intelligence integration, and automated response, it provides enterprises' SOC with an efficient and scalable phishing protection solution. In the current era of escalating attack methods, the concepts of proactive defense and intelligent decision-making will become important weapons for enterprises to resist cyber threats.