2025Full Stack Developer (Solo)In Progress

BotGuard Lab

Real-time bot detection using behavioral fingerprinting instead of simple rate-limiting.

Source code

MERN

Stack

Real-time

Detection

Security Platform

Type

Problem

Traditional bot mitigation, like IP rate-limits and CAPTCHAs, is easy to evade and often punishes real users. BotGuard Lab explores whether behavioral signals such as mouse movement, timing, and request patterns can flag automated traffic more reliably and with less friction for legitimate users.

Approach

Built a MERN app that ingests live traffic events over WebSocket and scores sessions against behavioral heuristics in real time.
Designed a dashboard that surfaces flagged sessions with the signals that triggered the flag, rather than a black-box score.
Kept the detection engine decoupled from the dashboard so the heuristics can evolve independently of the UI.

Key decisions

Heuristic scoring over a black-box model

Chose interpretable behavioral rules first so flagged traffic comes with a reason, which matters for anyone reviewing false positives.

WebSocket over polling

Real-time analytics needed sub-second updates; a WebSocket connection avoided the overhead of a polling loop per connected session.

Tech stack

MongoDBExpress.jsReactNode.js

Outcomes

Real-time dashboard with live behavioral analytics

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