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dizhaung's Projects

logparser icon logparser

Easy parsing of Apache HTTPD and NGINX access logs with Java, Hadoop, Hive, Pig, Flink, Beam, Storm, Drill, ...

logparser-1 icon logparser-1

A toolkit for automated log parsing [ICSE'19, TDSC'18, ICWS'17, DSN'16]

logstash icon logstash

OSSEC + Logstash + Elasticsearch + Kibana

logstash-1 icon logstash-1

Logstash - transport and process your logs, events, or other data

logstash-2 icon logstash-2

DEPRECATED; see https://www.elastic.co/guide/en/logstash/current/docker.html

logstash-forwarder icon logstash-forwarder

An experiment to cut logs in preparation for processing elsewhere. Replaced by Filebeat: https://github.com/elastic/beats/tree/master/filebeat

loki icon loki

Like Prometheus, but for logs.

lovinghome-real-estate-platform icon lovinghome-real-estate-platform

:zap:基于springboot+MyBatis+FreeMarker+redis+nginx+Echarts+druid等技术的JavaWeb项目------恋家房产平台(采用B/S架构,项目包含前后台,分为前台展示系统及后台管理系统。前台系统包含首页门户、登录注册、房产推荐、房产详情、热门房产、房产及小区搜索、经纪人列表及经纪机构创建、创建房产、房产百科、地图找房、用户个人中心、房产评论、房产打分等模块。 后台管理系统包含房产信息管理、用户管理、管理员管理、小区信息管理、博客管理、评论管理、经纪人管理、系统统计与多种图表展示、数据报表导入导出等模块。系统介绍及详细功能点、技术点见项目内文档描述)

lucene icon lucene

Apache Lucene open-source search software

luke icon luke

This is mavenised Luke: Lucene Toolbox Project

luyten icon luyten

An Open Source Java Decompiler Gui for Procyon

lynis icon lynis

Lynis - Security auditing tool for Linux, macOS, and UNIX-based systems. Assists with compliance testing (HIPAA/ISO27001/PCI DSS) and system hardening. Agentless, and installation optional.

machine-learning icon machine-learning

start learning of Machine Learning, one more step, one more to successed

machine-learning-1 icon machine-learning-1

:zap:机器学习实战(Python3):kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

machine-learning-algorithms-for-detecting-network-attacks-with-unsw-nb15-data-set icon machine-learning-algorithms-for-detecting-network-attacks-with-unsw-nb15-data-set

Due to the increasingly development of network technology recently, there are various cyber-attacks posed the huge threats to different fields around the world. Many studies and researches about cyber-security are carried out by experts in order to construct a safe network environment for people. The aim of the work is to build the detection models for classifying the attack data. Hence, we applied the UNSW-NB15 network data set which combines both normal and modern low-level attacks because we would like to create the experimental scenario close to the real world. Two classifiers are logistic regression and decision tree model for binary classification in the work. The deployed technique for decision tree achieved the highest result with 99.99% of testing accuracy compare to the 78.15% of logistic regression classifier. On the other hand, the KNN model is used for categorizing the multi-class in the project, and the averaged accuracy for testing is around 23% for ten categories classification.

mango icon mango

Common utilities for rapid application development

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