TechYu's repositories
ArDNSPod
基于DNSPod用户API实现的纯Shell动态域名客户端
arduino-slider
Arduino-based camera slider & trigger using a stepper motor.
awesome-algorithm
刷题训练指南
biometric-typing
Biometric keystroke classification using machine learning for user authentication
biometrics-authentication
Simple Biometric Authentication using keystroke dynamics(typing speed & pattern).
cc2640BleEpd
firmware and Android tool to update the epaper display on a cc2640 controlled shelf tag device
clover-x79-e5-2670-gtx650
Hackintosh clover perfect for El Capitan / Sierra / High Sierra
ddia
《Designing Data-Intensive Application》DDIA中文翻译
deeplearning.ai
deeplearning.ai , By Andrew Ng, All slide and notebook + data (without solution) and video link
domain_generation_algorithms
Some results of my DGA reversing efforts
druid
阿里云计算平台DataWorks团队出品，为监控而生的数据库连接池
E-ink-screen
An assistant to help you view text and picture calendars
epaper-msp430-clock
msp430 energia code for 2.13 inch e-Paper (v2) display
epaper_price_tag_mod
show image data to a epaper device originally deisgned for price tag
esp8266-weather-station-epaper
use esp8266 to show weather forecast on 2.9inch e-paper
flink-cdc-connectors
CDC Connectors for Apache Flink®
hanshow
Hanshow Products
Keystroke_dynamics
Different algorithms for user authentication based on keystroke dynamics of a user of mobile phone
mall
mall项目是一套电商系统，包括前台商城系统及后台管理系统，基于SpringBoot+MyBatis实现，采用Docker容器化部署。 前台商城系统包含首页门户、商品推荐、商品搜索、商品展示、购物车、订单流程、会员中心、客户服务、帮助中心等模块。 后台管理系统包含商品管理、订单管理、会员管理、促销管理、运营管理、内容管理、统计报表、财务管理、权限管理、设置等模块。
ML-Keystroke-Dynamics-using-Behavioural-Biometrics
This thesis focuses on the effective classification of the behavior of users accessing computing devices to authenticate them. The authentication is based on keystroke dynamics, which captures the users behavioral biometric and applies machine learning concepts to classify them. The users type a strong passcode ”.tie5Roanl” to record their typing pattern. In order to confirm identity, anonymous data from 94 users were collected to carry out the research. Given the raw data, features were extracted from the attributes based on the button pressed and action timestamp events. The support vector machine classifier uses multi-class classification with one vs. one decision shape function to classify different users. To reduce the classification error, it is essential to identify the important features from the raw data. In an effort to confront the generation of features from attributes an efficient feature extraction algorithm has been developed, obtaining high classification performance are now being sought. To handle the multi-class problem, the random forest classifier is used to identify the users effectively.
ML-Project
User Authentication using Keystroke dynamics
notepaper
桌面便签
pinduoduo_backdoor
拼多多apk内嵌提权代码，及动态下发dex分析
snow
看，下雪了~
speedtest-cli
Command line interface for testing internet bandwidth using speedtest.net
transcoder
docker部署hadoop+ffmpeg分布式转码系统