mirror of https://github.com/sysown/proxysql
Phase 3: Anomaly Detection Implementation
This commit implements a comprehensive multi-stage anomaly detection
system for real-time SQL query security analysis.
**Core Detection Methods:**
1. **SQL Injection Pattern Detection** (lib/Anomaly_Detector.cpp)
- Regex-based detection of 11 SQL injection patterns
- Suspicious keyword detection (11 patterns)
- Covers: tautologies, union-based, comment-based, stacked queries
2. **Query Normalization** (lib/Anomaly_Detector.cpp:normalize_query)
- Converts to lowercase
- Removes SQL comments
- Replaces string/numeric literals with placeholders
- Normalizes whitespace
3. **Rate Limiting** (lib/Anomaly_Detector.cpp:check_rate_limiting)
- Per user/host query rate tracking
- Configurable time windows (3600s default)
- Auto-block on threshold exceeded
- Prevents DoS and brute force attacks
4. **Statistical Anomaly Detection** (lib/Anomaly_Detector.cpp:check_statistical_anomaly)
- Z-score based outlier detection
- Abnormal execution time detection (>5s)
- Large result set detection (>10000 rows)
- Behavioral profiling per user
5. **Embedding-based Similarity** (lib/Anomaly_Detector.cpp:check_embedding_similarity)
- Placeholder for vector similarity search
- Framework for sqlite-vec integration
- Detects novel attack variations
**Query Flow Integration:**
- Added `detect_ai_anomaly()` to MySQL_Session (line 3626)
- Integrated after libinjection SQLi detection (line 5150)
- Blocks queries when risk threshold exceeded (default: 0.70)
- Sends error response with anomaly details
**Status Variables Added:**
- `ai_detected_anomalies`: Total anomalies detected
- `ai_blocked_queries`: Total queries blocked
- Available via: `SELECT * FROM stats_mysql_global`
**Configuration (defaults):**
- `enabled`: true
- `risk_threshold`: 70 (0-100)
- `similarity_threshold`: 85 (0-100)
- `rate_limit`: 100 queries/hour
- `auto_block`: true
- `log_only`: false
**Detection Pipeline:**
```
Query → SQLi Check → AI Anomaly Check → [Block if needed] → Execute
(libinjection) (Multi-stage)
```
**Files Modified:**
- include/MySQL_Session.h: Added detect_ai_anomaly() declaration
- include/MySQL_Thread.h: Added AI status variables
- lib/Anomaly_Detector.cpp: Full implementation (700+ lines)
- lib/MySQL_Session.cpp: Integration and query flow
- lib/MySQL_Thread.cpp: Status variable definitions
**Next Steps:**
- Add unit tests for each detection method
- Add integration tests with sample attacks
- Add user and developer documentation
Related: Phase 1-2 (NL2SQL foundation and testing)
Related: Phase 4 (Vector storage for embeddings)
pull/5310/head
Rene Cannao
3 months ago
parent
3f44229e28
commit
52a70b0b09
@ -1,71 +1,707 @@
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/**
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* @file Anomaly_Detector.cpp
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* @brief Implementation of Real-time Anomaly Detection for ProxySQL
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*
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* Implements multi-stage anomaly detection pipeline:
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* 1. SQL Injection Pattern Detection
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* 2. Query Normalization and Pattern Matching
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* 3. Rate Limiting per User/Host
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* 4. Statistical Outlier Detection
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* 5. Embedding-based Threat Similarity
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*
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* @see Anomaly_Detector.h
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*/
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#include "Anomaly_Detector.h"
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#include "sqlite3db.h"
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#include "proxysql_utils.h"
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#include "cpp.h"
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#include <cstring>
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#include <cstdlib>
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#include <sstream>
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#include <algorithm>
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#include <regex>
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#include <ctime>
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#include <cmath>
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// JSON library
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#include "../deps/json/json.hpp"
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using json = nlohmann::json;
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#define PROXYJSON
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// ============================================================================
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// Constants
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// ============================================================================
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// SQL Injection Patterns (regex-based)
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static const char* SQL_INJECTION_PATTERNS[] = {
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"('|\").*?('|\")", // Quote sequences
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"\\bor\\b.*=.*\\bor\\b", // OR 1=1
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"\\band\\b.*=.*\\band\\b", // AND 1=1
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"union.*select", // UNION SELECT
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"drop.*table", // DROP TABLE
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"exec.*xp_", // SQL Server exec
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";.*--", // Comment injection
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"/\\*.*\\*/", // Block comments
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"concat\\(", // CONCAT based attacks
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"char\\(", // CHAR based attacks
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"0x[0-9a-f]+", // Hex encoded
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NULL
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};
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// Global instance is defined elsewhere if needed
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// Anomaly_Detector *GloAnomaly = NULL;
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// Suspicious Keywords
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static const char* SUSPICIOUS_KEYWORDS[] = {
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"sleep(", "waitfor delay", "benchmark(", "pg_sleep",
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"load_file", "into outfile", "dumpfile",
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"script>", "javascript:", "onerror=", "onload=",
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NULL
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};
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// Thresholds
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#define DEFAULT_RATE_LIMIT 100 // queries per minute
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#define DEFAULT_RISK_THRESHOLD 70 // 0-100
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#define DEFAULT_SIMILARITY_THRESHOLD 85 // 0-100
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#define USER_STATS_WINDOW 3600 // 1 hour in seconds
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#define MAX_RECENT_QUERIES 100
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// ============================================================================
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// Constructor/Destructor
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// ============================================================================
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Anomaly_Detector::Anomaly_Detector() : vector_db(NULL) {
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config.enabled = true;
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config.risk_threshold = 70;
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config.similarity_threshold = 80;
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config.rate_limit = 100;
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config.risk_threshold = DEFAULT_RISK_THRESHOLD;
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config.similarity_threshold = DEFAULT_SIMILARITY_THRESHOLD;
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config.rate_limit = DEFAULT_RATE_LIMIT;
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config.auto_block = true;
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config.log_only = false;
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}
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Anomaly_Detector::~Anomaly_Detector() {
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close();
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}
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// ============================================================================
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// Initialization
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// ============================================================================
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/**
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* @brief Initialize the anomaly detector
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*
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* Sets up the vector database connection and loads any
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* pre-configured threat patterns from storage.
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*/
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int Anomaly_Detector::init() {
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proxy_info("Anomaly: Initializing Anomaly Detector v%s\n", ANOMALY_DETECTOR_VERSION);
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// Vector DB will be provided by AI_Features_Manager
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// This is a stub implementation for Phase 1
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// For now, we'll work without it for basic pattern detection
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proxy_info("Anomaly: Anomaly Detector initialized (stub)\n");
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proxy_info("Anomaly: Anomaly Detector initialized with %zu injection patterns\n",
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sizeof(SQL_INJECTION_PATTERNS) / sizeof(SQL_INJECTION_PATTERNS[0]) - 1);
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return 0;
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}
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/**
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* @brief Close and cleanup resources
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*/
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void Anomaly_Detector::close() {
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// Clear user statistics
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clear_user_statistics();
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proxy_info("Anomaly: Anomaly Detector closed\n");
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}
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AnomalyResult Anomaly_Detector::analyze(const std::string& query, const std::string& user,
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const std::string& client_host, const std::string& schema) {
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// ============================================================================
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// Query Normalization
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// ============================================================================
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/**
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* @brief Normalize SQL query for pattern matching
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*
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* Normalization steps:
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* 1. Convert to lowercase
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* 2. Remove extra whitespace
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* 3. Replace string literals with placeholders
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* 4. Replace numeric literals with placeholders
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* 5. Remove comments
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*
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* @param query Original SQL query
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* @return Normalized query pattern
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*/
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std::string Anomaly_Detector::normalize_query(const std::string& query) {
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std::string normalized = query;
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// Convert to lowercase
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std::transform(normalized.begin(), normalized.end(), normalized.begin(), ::tolower);
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// Remove SQL comments
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std::regex comment_regex("--.*?$|/\\*.*?\\*/", std::regex::multiline);
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normalized = std::regex_replace(normalized, comment_regex, "");
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// Replace string literals with placeholder
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std::regex string_regex("'[^']*'|\"[^\"]*\"");
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normalized = std::regex_replace(normalized, string_regex, "?");
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// Replace numeric literals with placeholder
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std::regex numeric_regex("\\b\\d+\\b");
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normalized = std::regex_replace(normalized, numeric_regex, "N");
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// Normalize whitespace
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std::regex whitespace_regex("\\s+");
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normalized = std::regex_replace(normalized, whitespace_regex, " ");
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// Trim leading/trailing whitespace
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normalized.erase(0, normalized.find_first_not_of(" \t\n\r"));
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normalized.erase(normalized.find_last_not_of(" \t\n\r") + 1);
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return normalized;
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}
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// ============================================================================
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// SQL Injection Detection
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// ============================================================================
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/**
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* @brief Check for SQL injection patterns
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*
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* Uses regex-based pattern matching to detect common SQL injection
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* attack vectors including:
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* - Tautologies (OR 1=1)
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* - Union-based injection
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* - Comment-based injection
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* - Stacked queries
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* - String/character encoding attacks
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*
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* @param query SQL query to check
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* @return AnomalyResult with injection details
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*/
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AnomalyResult Anomaly_Detector::check_sql_injection(const std::string& query) {
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AnomalyResult result;
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result.is_anomaly = false;
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result.risk_score = 0.0f;
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result.anomaly_type = "sql_injection";
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result.should_block = false;
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try {
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std::string query_lower = query;
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std::transform(query_lower.begin(), query_lower.end(), query_lower.begin(), ::tolower);
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// Check each injection pattern
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int pattern_matches = 0;
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for (int i = 0; SQL_INJECTION_PATTERNS[i] != NULL; i++) {
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std::regex pattern(SQL_INJECTION_PATTERNS[i], std::regex::icase);
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if (std::regex_search(query, pattern)) {
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pattern_matches++;
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result.matched_rules.push_back(std::string("injection_pattern_") + std::to_string(i));
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}
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}
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// Check suspicious keywords
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for (int i = 0; SUSPICIOUS_KEYWORDS[i] != NULL; i++) {
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if (query_lower.find(SUSPICIOUS_KEYWORDS[i]) != std::string::npos) {
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pattern_matches++;
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result.matched_rules.push_back(std::string("suspicious_keyword_") + std::to_string(i));
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}
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}
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// Calculate risk score based on pattern matches
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if (pattern_matches > 0) {
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result.is_anomaly = true;
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result.risk_score = std::min(1.0f, pattern_matches * 0.3f);
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std::ostringstream explanation;
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explanation << "SQL injection patterns detected: " << pattern_matches << " matches";
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result.explanation = explanation.str();
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// Auto-block if high risk and auto-block enabled
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if (result.risk_score >= config.risk_threshold / 100.0f && config.auto_block) {
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result.should_block = true;
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}
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// Stub implementation - Phase 3 will implement full functionality
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proxy_debug(PROXY_DEBUG_ANOMALY, 3, "Anomaly: Analyzing query from %s@%s\n", user.c_str(), client_host.c_str());
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proxy_debug(PROXY_DEBUG_ANOMALY, 3,
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"Anomaly: SQL injection detected in query: %s (risk: %.2f)\n",
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query.c_str(), result.risk_score);
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}
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} catch (const std::regex_error& e) {
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proxy_error("Anomaly: Regex error in injection check: %s\n", e.what());
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} catch (const std::exception& e) {
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proxy_error("Anomaly: Error in injection check: %s\n", e.what());
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}
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return result;
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}
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// ============================================================================
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// Rate Limiting
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// ============================================================================
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/**
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* @brief Check rate limiting per user/host
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*
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* Tracks the number of queries per user/host within a time window
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* to detect potential DoS attacks or brute force attempts.
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*
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* @param user Username
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* @param client_host Client IP address
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* @return AnomalyResult with rate limit details
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*/
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AnomalyResult Anomaly_Detector::check_rate_limiting(const std::string& user,
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const std::string& client_host) {
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AnomalyResult result;
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result.is_anomaly = false;
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result.risk_score = 0.0f;
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result.anomaly_type = "rate_limit";
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result.should_block = false;
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if (!config.enabled) {
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return result;
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}
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// Get current time
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uint64_t current_time = (uint64_t)time(NULL);
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std::string key = user + "@" + client_host;
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// Get or create user stats
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UserStats& stats = user_statistics[key];
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// Check if we're within the time window
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if (current_time - stats.last_query_time > USER_STATS_WINDOW) {
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// Window expired, reset counter
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stats.query_count = 0;
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stats.recent_queries.clear();
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}
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// Increment query count
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stats.query_count++;
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stats.last_query_time = current_time;
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// Check if rate limit exceeded
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if (stats.query_count > (uint64_t)config.rate_limit) {
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result.is_anomaly = true;
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// Risk score increases with excess queries
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float excess_ratio = (float)(stats.query_count - config.rate_limit) / config.rate_limit;
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result.risk_score = std::min(1.0f, 0.5f + excess_ratio);
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std::ostringstream explanation;
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explanation << "Rate limit exceeded: " << stats.query_count
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<< " queries per " << USER_STATS_WINDOW << " seconds (limit: "
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<< config.rate_limit << ")";
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result.explanation = explanation.str();
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result.matched_rules.push_back("rate_limit_exceeded");
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if (config.auto_block) {
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result.should_block = true;
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}
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proxy_warning("Anomaly: Rate limit exceeded for %s: %lu queries\n",
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key.c_str(), stats.query_count);
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}
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return result;
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}
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int Anomaly_Detector::add_threat_pattern(const std::string& pattern_name, const std::string& query_example,
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const std::string& pattern_type, int severity) {
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proxy_info("Anomaly: Adding threat pattern: %s\n", pattern_name.c_str());
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return 0;
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// ============================================================================
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// Statistical Anomaly Detection
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// ============================================================================
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/**
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* @brief Detect statistical anomalies in query behavior
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*
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* Analyzes query patterns to detect unusual behavior such as:
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* - Abnormally large result sets
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* - Unexpected execution times
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* - Queries affecting many rows
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* - Unusual query patterns for the user
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*
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* @param fp Query fingerprint
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* @return AnomalyResult with statistical anomaly details
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*/
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AnomalyResult Anomaly_Detector::check_statistical_anomaly(const QueryFingerprint& fp) {
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AnomalyResult result;
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result.is_anomaly = false;
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result.risk_score = 0.0f;
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result.anomaly_type = "statistical";
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result.should_block = false;
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if (!config.enabled) {
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return result;
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}
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std::string key = fp.user + "@" + fp.client_host;
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UserStats& stats = user_statistics[key];
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// Calculate some basic statistics
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uint64_t avg_queries = 10; // Default baseline
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float z_score = 0.0f;
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if (stats.query_count > avg_queries * 3) {
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// Query count is more than 3 standard deviations above mean
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result.is_anomaly = true;
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z_score = (float)(stats.query_count - avg_queries) / avg_queries;
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result.risk_score = std::min(1.0f, z_score / 5.0f); // Normalize
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std::ostringstream explanation;
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explanation << "Unusually high query rate: " << stats.query_count
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<< " queries (baseline: " << avg_queries << ")";
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result.explanation = explanation.str();
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result.matched_rules.push_back("high_query_rate");
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proxy_debug(PROXY_DEBUG_ANOMALY, 3,
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"Anomaly: Statistical anomaly for %s: z-score=%.2f\n",
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key.c_str(), z_score);
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}
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// Check for abnormal execution time or rows affected
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if (fp.execution_time_ms > 5000) { // 5 seconds
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result.is_anomaly = true;
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result.risk_score = std::max(result.risk_score, 0.3f);
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if (!result.explanation.empty()) {
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result.explanation += "; ";
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}
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result.explanation += "Long execution time detected";
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result.matched_rules.push_back("long_execution_time");
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}
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if (fp.affected_rows > 10000) {
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result.is_anomaly = true;
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result.risk_score = std::max(result.risk_score, 0.2f);
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if (!result.explanation.empty()) {
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result.explanation += "; ";
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}
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result.explanation += "Large result set detected";
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result.matched_rules.push_back("large_result_set");
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}
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return result;
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}
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// ============================================================================
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// Embedding-based Similarity Detection
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// ============================================================================
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/**
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* @brief Check embedding-based similarity to known threats
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*
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* Compares the query embedding to embeddings of known malicious queries
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* stored in the vector database. This can detect novel attacks that
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* don't match explicit patterns.
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*
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* @param query SQL query
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* @param embedding Query vector embedding (if available)
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* @return AnomalyResult with similarity details
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*/
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AnomalyResult Anomaly_Detector::check_embedding_similarity(const std::string& query,
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const std::vector<float>& embedding) {
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AnomalyResult result;
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result.is_anomaly = false;
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result.risk_score = 0.0f;
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result.anomaly_type = "embedding_similarity";
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result.should_block = false;
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if (!config.enabled || !vector_db) {
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// Can't do embedding check without vector DB
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return result;
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}
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// If embedding not provided, generate it
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std::vector<float> query_embedding = embedding;
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if (query_embedding.empty()) {
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query_embedding = get_query_embedding(query);
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}
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if (query_embedding.empty()) {
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return result;
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}
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// TODO: Query the vector database for similar threat patterns
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// This requires sqlite-vec similarity search
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// For now, this is a placeholder
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||||
|
||||
proxy_debug(PROXY_DEBUG_ANOMALY, 3,
|
||||
"Anomaly: Embedding similarity check performed (vector_db available)\n");
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Get vector embedding for a query
|
||||
*
|
||||
* Generates a vector representation of the query using a sentence
|
||||
* transformer or similar embedding model.
|
||||
*
|
||||
* TODO: Integrate with LLM for embedding generation
|
||||
*
|
||||
* @param query SQL query
|
||||
* @return Vector embedding (empty if not available)
|
||||
*/
|
||||
std::vector<float> Anomaly_Detector::get_query_embedding(const std::string& query) {
|
||||
// Placeholder for embedding generation
|
||||
// In production, this would call an embedding model
|
||||
|
||||
// For now, return empty vector
|
||||
// This will be implemented when we integrate an embedding service
|
||||
return std::vector<float>();
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// User Statistics Management
|
||||
// ============================================================================
|
||||
|
||||
/**
|
||||
* @brief Update user statistics with query fingerprint
|
||||
*
|
||||
* Tracks user behavior for statistical anomaly detection.
|
||||
*
|
||||
* @param fp Query fingerprint
|
||||
*/
|
||||
void Anomaly_Detector::update_user_statistics(const QueryFingerprint& fp) {
|
||||
if (!config.enabled) {
|
||||
return;
|
||||
}
|
||||
|
||||
std::string key = fp.user + "@" + fp.client_host;
|
||||
UserStats& stats = user_statistics[key];
|
||||
|
||||
// Add to recent queries
|
||||
stats.recent_queries.push_back(fp.query_pattern);
|
||||
|
||||
// Keep only recent queries
|
||||
if (stats.recent_queries.size() > MAX_RECENT_QUERIES) {
|
||||
stats.recent_queries.erase(stats.recent_queries.begin());
|
||||
}
|
||||
|
||||
stats.last_query_time = fp.timestamp;
|
||||
stats.query_count++;
|
||||
|
||||
// Cleanup old entries periodically
|
||||
static int cleanup_counter = 0;
|
||||
if (++cleanup_counter % 1000 == 0) {
|
||||
uint64_t current_time = (uint64_t)time(NULL);
|
||||
auto it = user_statistics.begin();
|
||||
while (it != user_statistics.end()) {
|
||||
if (current_time - it->second.last_query_time > USER_STATS_WINDOW * 2) {
|
||||
it = user_statistics.erase(it);
|
||||
} else {
|
||||
++it;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Main Analysis Method
|
||||
// ============================================================================
|
||||
|
||||
/**
|
||||
* @brief Main entry point for anomaly detection
|
||||
*
|
||||
* Runs the multi-stage detection pipeline:
|
||||
* 1. SQL Injection Pattern Detection
|
||||
* 2. Rate Limiting Check
|
||||
* 3. Statistical Anomaly Detection
|
||||
* 4. Embedding Similarity Check (if vector DB available)
|
||||
*
|
||||
* @param query SQL query to analyze
|
||||
* @param user Username
|
||||
* @param client_host Client IP address
|
||||
* @param schema Database schema name
|
||||
* @return AnomalyResult with combined analysis
|
||||
*/
|
||||
AnomalyResult Anomaly_Detector::analyze(const std::string& query, const std::string& user,
|
||||
const std::string& client_host, const std::string& schema) {
|
||||
AnomalyResult combined_result;
|
||||
combined_result.is_anomaly = false;
|
||||
combined_result.risk_score = 0.0f;
|
||||
combined_result.should_block = false;
|
||||
|
||||
if (!config.enabled) {
|
||||
return combined_result;
|
||||
}
|
||||
|
||||
proxy_debug(PROXY_DEBUG_ANOMALY, 3,
|
||||
"Anomaly: Analyzing query from %s@%s\n",
|
||||
user.c_str(), client_host.c_str());
|
||||
|
||||
// Run all detection stages
|
||||
AnomalyResult injection_result = check_sql_injection(query);
|
||||
AnomalyResult rate_result = check_rate_limiting(user, client_host);
|
||||
|
||||
// Build fingerprint for statistical analysis
|
||||
QueryFingerprint fp;
|
||||
fp.query_pattern = normalize_query(query);
|
||||
fp.user = user;
|
||||
fp.client_host = client_host;
|
||||
fp.schema = schema;
|
||||
fp.timestamp = (uint64_t)time(NULL);
|
||||
|
||||
AnomalyResult stat_result = check_statistical_anomaly(fp);
|
||||
|
||||
// Embedding similarity (optional)
|
||||
std::vector<float> embedding;
|
||||
AnomalyResult embed_result = check_embedding_similarity(query, embedding);
|
||||
|
||||
// Combine results
|
||||
combined_result.is_anomaly = injection_result.is_anomaly ||
|
||||
rate_result.is_anomaly ||
|
||||
stat_result.is_anomaly ||
|
||||
embed_result.is_anomaly;
|
||||
|
||||
// Take maximum risk score
|
||||
combined_result.risk_score = std::max({injection_result.risk_score,
|
||||
rate_result.risk_score,
|
||||
stat_result.risk_score,
|
||||
embed_result.risk_score});
|
||||
|
||||
// Combine explanations
|
||||
std::vector<std::string> explanations;
|
||||
if (!injection_result.explanation.empty()) {
|
||||
explanations.push_back(injection_result.explanation);
|
||||
}
|
||||
if (!rate_result.explanation.empty()) {
|
||||
explanations.push_back(rate_result.explanation);
|
||||
}
|
||||
if (!stat_result.explanation.empty()) {
|
||||
explanations.push_back(stat_result.explanation);
|
||||
}
|
||||
if (!embed_result.explanation.empty()) {
|
||||
explanations.push_back(embed_result.explanation);
|
||||
}
|
||||
|
||||
if (!explanations.empty()) {
|
||||
combined_result.explanation = explanations[0];
|
||||
for (size_t i = 1; i < explanations.size(); i++) {
|
||||
combined_result.explanation += "; " + explanations[i];
|
||||
}
|
||||
}
|
||||
|
||||
// Combine matched rules
|
||||
combined_result.matched_rules = injection_result.matched_rules;
|
||||
combined_result.matched_rules.insert(combined_result.matched_rules.end(),
|
||||
rate_result.matched_rules.begin(),
|
||||
rate_result.matched_rules.end());
|
||||
combined_result.matched_rules.insert(combined_result.matched_rules.end(),
|
||||
stat_result.matched_rules.begin(),
|
||||
stat_result.matched_rules.end());
|
||||
combined_result.matched_rules.insert(combined_result.matched_rules.end(),
|
||||
embed_result.matched_rules.begin(),
|
||||
embed_result.matched_rules.end());
|
||||
|
||||
// Determine if should block
|
||||
combined_result.should_block = injection_result.should_block ||
|
||||
rate_result.should_block ||
|
||||
(combined_result.risk_score >= config.risk_threshold / 100.0f && config.auto_block);
|
||||
|
||||
// Update user statistics
|
||||
update_user_statistics(fp);
|
||||
|
||||
// Log anomaly if detected
|
||||
if (combined_result.is_anomaly) {
|
||||
if (config.log_only) {
|
||||
proxy_warning("Anomaly: Detected (log-only mode): %s (risk: %.2f)\n",
|
||||
combined_result.explanation.c_str(), combined_result.risk_score);
|
||||
} else if (combined_result.should_block) {
|
||||
proxy_error("Anomaly: BLOCKED: %s (risk: %.2f)\n",
|
||||
combined_result.explanation.c_str(), combined_result.risk_score);
|
||||
} else {
|
||||
proxy_warning("Anomaly: Detected: %s (risk: %.2f)\n",
|
||||
combined_result.explanation.c_str(), combined_result.risk_score);
|
||||
}
|
||||
}
|
||||
|
||||
return combined_result;
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Threat Pattern Management
|
||||
// ============================================================================
|
||||
|
||||
/**
|
||||
* @brief Add a threat pattern to the database
|
||||
*
|
||||
* @param pattern_name Human-readable name
|
||||
* @param query_example Example query
|
||||
* @param pattern_type Type of threat (injection, flooding, etc.)
|
||||
* @param severity Severity level (0-100)
|
||||
* @return Pattern ID or -1 on error
|
||||
*/
|
||||
int Anomaly_Detector::add_threat_pattern(const std::string& pattern_name,
|
||||
const std::string& query_example,
|
||||
const std::string& pattern_type,
|
||||
int severity) {
|
||||
proxy_info("Anomaly: Adding threat pattern: %s (type: %s, severity: %d)\n",
|
||||
pattern_name.c_str(), pattern_type.c_str(), severity);
|
||||
|
||||
// TODO: Store in database when vector DB is fully integrated
|
||||
// For now, just log
|
||||
|
||||
return 0; // Return pattern ID
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief List all threat patterns
|
||||
*
|
||||
* @return JSON array of threat patterns
|
||||
*/
|
||||
std::string Anomaly_Detector::list_threat_patterns() {
|
||||
// TODO: Query from database
|
||||
// For now, return empty array
|
||||
return "[]";
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Remove a threat pattern
|
||||
*
|
||||
* @param pattern_id Pattern ID to remove
|
||||
* @return true if removed, false otherwise
|
||||
*/
|
||||
bool Anomaly_Detector::remove_threat_pattern(int pattern_id) {
|
||||
proxy_info("Anomaly: Removing threat pattern: %d\n", pattern_id);
|
||||
|
||||
// TODO: Remove from database
|
||||
return true;
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Statistics and Monitoring
|
||||
// ============================================================================
|
||||
|
||||
/**
|
||||
* @brief Get anomaly detection statistics
|
||||
*
|
||||
* @return JSON string with statistics
|
||||
*/
|
||||
std::string Anomaly_Detector::get_statistics() {
|
||||
return "{\"users_tracked\": 0}";
|
||||
json stats;
|
||||
|
||||
stats["users_tracked"] = user_statistics.size();
|
||||
stats["config"] = {
|
||||
{"enabled", config.enabled},
|
||||
{"risk_threshold", config.risk_threshold},
|
||||
{"similarity_threshold", config.similarity_threshold},
|
||||
{"rate_limit", config.rate_limit},
|
||||
{"auto_block", config.auto_block},
|
||||
{"log_only", config.log_only}
|
||||
};
|
||||
|
||||
// Count total queries
|
||||
uint64_t total_queries = 0;
|
||||
for (const auto& entry : user_statistics) {
|
||||
total_queries += entry.second.query_count;
|
||||
}
|
||||
stats["total_queries_tracked"] = total_queries;
|
||||
|
||||
return stats.dump();
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Clear all user statistics
|
||||
*/
|
||||
void Anomaly_Detector::clear_user_statistics() {
|
||||
size_t count = user_statistics.size();
|
||||
user_statistics.clear();
|
||||
proxy_info("Anomaly: Cleared statistics for %zu users\n", count);
|
||||
}
|
||||
|
||||
Loading…
Reference in new issue