统一用户行为分析与实时异常检测，提升安全与体验
Trackly: A Unified SaaS Platform for User Behavior Analytics and Real Time Rule Based Anomaly Detection
- 融合行为分析与规则驱动的实时异常检测
- 合成数据测试中准确率98.1%，误报仅2.25%
- 适合电商和中小企业快速部署使用
理解用户行为对优化数字体验、提升转化率及防范账户劫持、欺诈和机器人攻击至关重要。现有平台常将产品分析与安全功能分离，导致信息碎片化且威胁响应延迟。Trackly 是一个可扩展的 SaaS 平台，整合全面的用户行为分析与实时规则驱动的异常检测。它追踪会话、基于 IP 的地理位置、设备浏览器指纹，以及页面浏览、加购、结算等细粒度事件。通过可配置规则识别可疑行为，如新设备/位置登录、不可能旅行（使用哈弗林公式）、高速机器人式操作、使用 VPN 代理或单个 IP 多账号，并采用加权风险评分实现透明可解释的决策。实时仪表盘展示全局会话地图、日活/月活、跳出率和会话时长。通过轻量级 JavaScript SDK 和安全 REST API 简化集成。基于多租户微服务架构（ASP.NET Core、MongoDB、RabbitMQ、Next.js），在合成数据集上达到 98.1% 准确率、97.7% 精确率和 2.25% 误报率，验证了其对中小企业和电商场景的高效性。
原文摘要 · Abstract (English)
Understanding user behavior is essential for improving digital experiences, optimizing business conversions, and mitigating threats like account takeovers, fraud, and bot attacks. Most platforms separate product analytics and security, creating fragmented visibility and delayed threat detection. Trackly, a scalable SaaS platform, unifies comprehensive user behavior analytics with real time, rule based anomaly detection. It tracks sessions, IP based geo location, device browser fingerprints, and granular events such as page views, add to cart, and checkouts. Suspicious activities logins from new devices or locations, impossible travel (Haversine formula), rapid bot like actions, VPN proxy usage, or multiple accounts per IP are flagged via configurable rules with weighted risk scoring, enabling transparent, explainable decisions. A real time dashboard provides global session maps, DAU MAU, bounce rates, and session durations. Integration is simplified with a lightweight JavaScript SDK and secure REST APIs. Implemented on a multi tenant microservices stack (ASP.NET Core, MongoDB, RabbitMQ, Next.js), Trackly achieved 98.1% accuracy, 97.7% precision, and 2.25% false positives on synthetic datasets, proving its efficiency for SMEs and ecommerce.
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