> ## Documentation Index > Fetch the complete documentation index at: https://docs.knoxcall.com/llms.txt > Use this file to discover all available pages before exploring further. # AI-Powered Threat Detection > Intelligent threat detection and security monitoring powered by machine learning. Automatically identify suspicious patterns, anomalies, and potential attacks. **PLANNED FEATURE - NOT YET AVAILABLE** This feature is currently in development and planned for release in Q3 2025. The documentation below describes the planned functionality and is subject to change. For current security features, see [Rate Limiting](/advanced/rate-limiting), [Request Signing](/security/request-signing), and [OAuth2 Security](/security/oauth2-flow). # AI-Powered Threat Detection Protect your APIs with intelligent threat detection. KnoxCall's AI engine analyzes request patterns in real-time to identify suspicious activity, potential attacks, and security anomalies before they cause damage. ## What is AI Threat Detection? Traditional security relies on manual rules and fixed thresholds. AI Threat Detection uses machine learning to: * 🧠 **Learn normal behavior** for your APIs * 🚨 **Detect anomalies** automatically * 🎯 **Identify attack patterns** (SQL injection, XSS, brute force) * 🔮 **Predict threats** before they escalate * 📊 **Adapt continuously** as your traffic evolves ## How It Works ### 1. Baseline Learning KnoxCall AI learns your normal traffic patterns: ```text theme={"dark"} Week 1: Learning Phase ├─ Normal request volume: 1,000-5,000/hour ├─ Common endpoints: /api/users, /api/orders ├─ Typical latency: 50-200ms ├─ Standard error rate: 1-2% └─ Geographic distribution: 60% US, 30% EU, 10% APAC ``` ### 2. Anomaly Detection AI compares real-time traffic against baseline: ```text theme={"dark"} Current Activity: ├─ Request volume: 50,000/hour ⚠️ (10x normal) ├─ New endpoint: /api/admin/delete-all ⚠️ ├─ Latency: 5,000ms ⚠️ (10x normal) ├─ Error rate: 45% ⚠️ (23x normal) └─ Location: Russia ⚠️ (never seen before) AI Assessment: 🚨 HIGH THREAT PROBABILITY ``` ### 3. Threat Classification AI categorizes threats by type: ```text theme={"dark"} 🔴 Critical: Active attack in progress 🟠 High: Suspicious pattern detected 🟡 Medium: Unusual activity 🟢 Low: Minor anomaly ``` ### 4. Automated Response Based on threat level, KnoxCall can: * **Alert** admins via email/Slack/SMS * **Rate limit** suspicious IPs * **Block** confirmed attacks * **Log** detailed forensics * **Generate** security reports ## Detected Threat Types ### 1. Brute Force Attacks **Pattern:** ```text theme={"dark"} IP: 192.168.1.100 Request: POST /api/login Status: 401 Unauthorized Frequency: 1,000 attempts in 5 minutes ``` **AI Detection:** * Rapid successive failures * Same IP, different credentials * Exponential retry pattern **Response:** * Block IP after 10 failed attempts * Rate limit: 1 request/minute * Alert security team ### 2. SQL Injection Attempts **Pattern:** ```text theme={"dark"} GET /api/users?id=1' OR '1'='1 GET /api/search?q=admin'-- POST /api/data {"name": "test'; DROP TABLE users;--"} ``` **AI Detection:** * SQL keywords in parameters * Quote and comment patterns * UNION, DROP, SELECT in unexpected places **Response:** * Block request immediately * Log full payload * Alert with OWASP category ### 3. API Abuse / Scraping **Pattern:** ```text theme={"dark"} IP: 203.0.113.50 Requests: 10,000 in 10 minutes Endpoints: /api/products/1, /api/products/2, ... /api/products/10000 User-Agent: python-requests/2.28.0 ``` **AI Detection:** * Sequential ID enumeration * Suspicious user agent * No typical browsing pattern * Ignores rate limit headers **Response:** * Rate limit aggressively * Require CAPTCHA * Block after threshold ### 4. Credential Stuffing **Pattern:** ```text theme={"dark"} 1000 login attempts with: - Different usernames - Different passwords - Same IP or distributed botnet - Known leaked credential combinations ``` **AI Detection:** * Cross-reference with breach databases * Unusual geographic distribution * Coordinated timing * Low success rate **Response:** * Block IP ranges * Require 2FA * Force password reset ### 5. DDoS Attacks **Pattern:** ```text theme={"dark"} Sudden traffic spike: - Normal: 1,000 req/min - Attack: 100,000 req/min - From: 10,000 unique IPs - Target: Single endpoint ``` **AI Detection:** * Abnormal traffic volume * Distributed sources * Identical request patterns * Low latency variance (bots) **Response:** * Activate DDoS mitigation * Enable challenge response * Rate limit globally * Contact CDN/ISP ### 6. Account Takeover Attempts **Pattern:** ```text theme={"dark"} User login from: - New device - New location (5,000 miles away) - Within 5 minutes of previous login - Different user agent ``` **AI Detection:** * Impossible travel * Device fingerprint mismatch * Unusual access patterns * Credential change attempts **Response:** * Require additional verification * Lock account temporarily * Alert user via email * Log forensic details ## AI Features ### Behavioral Analysis Track individual client behavior: ```text theme={"dark"} Client: mobile-app-ios Normal Behavior: ├─ Active: 9 AM - 9 PM EST ├─ Endpoints: /api/feed, /api/profile, /api/posts ├─ Frequency: 10-50 requests/hour ├─ Locations: US, Canada └─ Success rate: 98% Anomaly Detected: ├─ Time: 3 AM EST ⚠️ ├─ Endpoint: /api/admin/users ⚠️ ├─ Frequency: 500 requests/minute ⚠️ ├─ Location: Unknown VPN ⚠️ └─ Success rate: 20% ⚠️ Threat Level: 🔴 CRITICAL ``` ### Pattern Recognition Identify complex attack patterns: ```text theme={"dark"} Attack Pattern: Advanced Persistent Threat (APT) ├─ Phase 1: Reconnaissance (scanning endpoints) ├─ Phase 2: Exploitation (SQL injection attempts) ├─ Phase 3: Privilege escalation (admin API calls) └─ Phase 4: Data exfiltration (bulk downloads) AI detected all 4 phases and blocked at Phase 2 ``` ### Predictive Alerts Get warnings before attacks happen: ```text theme={"dark"} 🔮 Predictive Alert: Potential Attack ├─ Confidence: 85% ├─ Type: Brute force attack ├─ Target: /api/login ├─ Predicted timing: Next 30 minutes └─ Recommended action: Enable rate limiting ``` ## Configuration ### Step 1: Enable AI Threat Detection 1. Navigate to **Security** → **AI Threat Detection** 2. Toggle **Enable AI Detection** to ON 3. Choose sensitivity level: ```text theme={"dark"} Low: Only critical threats Medium: High and critical threats (recommended) High: All anomalies ``` ### Step 2: Configure Response Actions Set automated responses: ```yaml theme={"dark"} Critical Threats: - Block IP immediately - Send SMS alert - Log to SIEM High Threats: - Rate limit to 10 req/min - Send email alert - Monitor for 1 hour Medium Threats: - Log and monitor - Send daily summary Low Threats: - Log only ``` ### Step 3: Define Protected Routes Choose which routes to protect: ```text theme={"dark"} Route: payment-api AI Protection: Maximum Response: Block on threat Route: public-api AI Protection: Medium Response: Rate limit on threat Route: internal-tools AI Protection: Low Response: Alert only ``` ## Security Dashboard ### Threat Overview Real-time threat monitoring: ```text theme={"dark"} 🟢 System Status: SECURE 📊 Threats Detected (24h): 47 🚫 Attacks Blocked: 38 ⚠️ Active Investigations: 2 ``` ### Threat Feed Live feed of detected threats: ```text theme={"dark"} [12:45:32] 🔴 CRITICAL: SQL Injection blocked IP: 203.0.113.100 Route: /api/search Payload: admin'-- Action: Blocked [12:42:15] 🟠 HIGH: Brute force detected IP: 198.51.100.50 Route: /api/login Attempts: 50 in 2 minutes Action: Rate limited [12:38:04] 🟡 MEDIUM: Unusual traffic pattern Client: mobile-app-android Volume: 5x normal Action: Monitoring ``` ### Threat Map Geographic visualization of threats: ```text theme={"dark"} 🔴 5 attacks (Europe) │ 🟠 12 threats (North America) │ 🟡 3 anomalies (Asia Pacific) ``` ## Integration with External Tools ### SIEM Integration Send threat data to your SIEM: ```text theme={"dark"} Configure Splunk HTTP Event Collector Send threats as JSON events Create dashboards and alerts ``` ```text theme={"dark"} Use Datadog API integration Send threat events as logs Create security monitors ``` ```text theme={"dark"} Configure HTTP source Stream threat data Build security analytics ``` ### Webhook Notifications Send threats to custom endpoints: ```javascript theme={"dark"} POST https://your-security-system.com/threats { "threat_id": "thr_abc123", "severity": "critical", "type": "sql_injection", "ip": "203.0.113.100", "route": "/api/search", "timestamp": "2025-01-20T15:30:00Z", "details": { "payload": "admin'--", "action_taken": "blocked" } } ``` ## Machine Learning Models KnoxCall uses multiple ML models: ### 1. Anomaly Detection Model **Algorithm**: Isolation Forest **Training**: Continuous on your traffic **Features**: 50+ traffic characteristics **Accuracy**: 95%+ true positive rate ### 2. Threat Classification Model **Algorithm**: Gradient Boosting (XGBoost) **Training**: 10M+ labeled attack samples **Output**: Threat type and confidence **Accuracy**: 98% classification accuracy ### 3. Pattern Recognition Model **Algorithm**: LSTM Neural Network **Purpose**: Detect multi-stage attacks **Window**: Analyzes 1-hour sequences **Accuracy**: 92% attack chain detection ## Best Practices ### 1. Start with Learning Mode Let AI learn for 1-2 weeks before enforcing: ```text theme={"dark"} Week 1-2: Learning mode (alerts only) Week 3+: Enforcement mode (block threats) ``` ### 2. Review False Positives Check alerts weekly: ```text theme={"dark"} False Positive Rate: < 1% Review: Weekly Action: Whitelist legitimate patterns ``` ### 3. Tune Sensitivity Adjust based on your risk tolerance: ```text theme={"dark"} High Security: High sensitivity Balanced: Medium sensitivity (default) Performance Critical: Low sensitivity ``` ### 4. Combine with Other Security AI works best alongside: * ✅ Rate limiting * ✅ IP whitelisting * ✅ Request signing * ✅ WAF rules ### 5. Monitor and Adjust Review AI performance monthly: ```text theme={"dark"} Metrics to track: - Detection rate - False positive rate - Response time - Blocked threats ``` ## Troubleshooting ### High false positive rate **Solutions:** * Lower sensitivity * Whitelist known-good IPs * Add legitimate patterns to training ### Attacks getting through **Solutions:** * Increase sensitivity * Enable additional security layers * Update threat intelligence feeds ### Performance impact **Solutions:** * Enable sampling (analyze 10% of traffic) * Use async threat detection * Upgrade to higher tier ## Next Steps Add cryptographic verification Complement AI with rate limits Configure threat alerts View security analytics *** * **Level**: advanced * **Time**: 20 minutes `security`, `ai`, `ml`, `threat-detection`, `protection`