benhe119's repositories
attack-arsenal
A collection of red team and adversary emulation resources developed and released by MITRE.
bearded-avenger
CIF v3 -- the fastest way to consume threat intelligence
binsnitch
Detect silent (unwanted) changes to files on your system
click
The Click modular router: fast modular packet processing and analysis
constellation_cyber_plugins
The ACSC CyberTools Plugins are build upon the functionality of the Constellation data visualisation platform to deliver enrichments suited the cyber security community
dam
Pegler uses Faucet SDN controller to react based on Zeek IDS notification.
DGFraud
A Deep Graph-based Toolbox for Fraud Detection
fame
FAME Automates Malware Evaluation
flin_userPaint
基于Flink流处理的动态实时亿级全端用户画像系统
fraud-detection
Fraud Detection using Classification and Graph Database
Geeker-Tips
Geeker 杂记
gofir
Collection of command-line tools written in Golang for Forensics and Incident Response
gryffin
Gryffin is a large scale web security scanning platform.
hugegraph-client
HugeGraph Database client for Java
hugegraph-doc
HugeGraph Database user documentation
hugegraph-hubble
A graph management and analysis platform that provides features: graph data load, schema management, graph relationship analysis and graphical display, and more.
hugegraph-loader
HugeGraph Database data loader
hugegraph-tools
HugeGraph Database deploy and manage tool
OPCDE
OPCDE Cybersecurity Conference Materials
paseto
Platform-Agnostic Security Tokens
sandboxie
The Sandboxie application
SmartGridFraudDetection
Electricity Fraud Detection in Smart Grids
sriracha-iq
Rapid cybersecurity toolkit based on Elastic in Docker. Designed to quickly build elastic-based environments to analyze and execute threat hunting, blue team assessments, audits, and security control assessments.
System-Vulnerability
系统漏洞合集 Since 2019-10-16
UserBehaviorAnalysis
模拟电商系统上线运行一段时间后，根据收集到大量的用户行为数据，利用大数据技术（Flink）进行深入挖掘和分析，进而得到感兴趣的商业指标并增强对风险的控制。 整体可以分为用户行为习惯数据和业务行为数据两大类。用户的行为习惯数据包括了用户的登录方式、上线的时间点及时长、点击和浏览页面、页面停留时间以及页面跳转等等，从中进行流量统计和热门商品的统计，并深入挖掘用户的特征；业务行为数据分为两类：一类是能够明显地表现出用户兴趣的行为，比如对商品的收藏、喜欢、评分和评价，对数据进行深入分析，得到用户画像，进而对用户给出个性化的推荐商品列表；另一类则是常规的业务操作，关注异常状况以做好风控，比如登录和订单支付。
zlogging
Bro/Zeek logging framework for Python