Tagged articles

device fingerprint

4 articles · Page 1 of 1

Jul 16, 2026 · Backend Development

How to Distinguish Real Users from Scalper Bots in Flash Sale Systems

The article presents a comprehensive five‑layer risk‑control architecture—covering front‑end behavior verification, device fingerprinting, account profiling, IP network analysis, and backend request sequencing—designed to separate genuine shoppers from scalper scripts during high‑traffic flash‑sale events, using Redis‑based storage and dynamic thresholds to minimize false positives.

Redisbackendbot detection

0 likes · 21 min read

How to Distinguish Real Users from Scalper Bots in Flash Sale Systems

Java Web Project

Apr 18, 2026 · Information Security

How a Midnight SMS Scam Revealed the Need for a Multi‑Layer Anti‑Abuse System

A night‑time SMS billing attack that drained ¥11,500 in two hours exposed flaws in a naïve Session‑based verification design, prompting a detailed, five‑layer defense architecture that combines gateway rate limiting, Redis token‑bucket controls, advanced captcha tracking, device fingerprinting, blacklist automation, and honey‑pot tactics to raise attack costs.

RedisSecurityanti-abuse

0 likes · 14 min read

How a Midnight SMS Scam Revealed the Need for a Multi‑Layer Anti‑Abuse System

Qu Tech

Aug 23, 2021 · Information Security

How Qutoutiao Built a Robust Anti-Cheat SDK and Device Fingerprinting System

This article outlines Qutoutiao's security team's development of a standalone anti‑cheat SDK, its custom device‑fingerprinting architecture, the evolution of their fingerprint algorithms, the creation of the tuid identifier, and how these technologies together combat black‑market attacks while maintaining compliance and stability.

SDKanti-cheatdevice fingerprint

0 likes · 18 min read

How Qutoutiao Built a Robust Anti-Cheat SDK and Device Fingerprinting System

NetEase Smart Enterprise Tech+

Mar 9, 2021 · Information Security

How Server‑Side Device Fingerprinting Boosts Security and Stability

Device fingerprinting uniquely identifies devices using collected data; this article explains how uniqueness and stability are measured, shows probability‑based calculations for single and combined fields, discusses the shortcomings of client‑side methods, and details a server‑side multi‑algorithm approach that improves security and stability.

Securityanti-frauddecision tree

0 likes · 11 min read

How Server‑Side Device Fingerprinting Boosts Security and Stability