Mosand's Stars
Nir-J/ML-Projects
Basic Machine learning projects and assignments done by me.
tsaiflow/KDD-FeatureSelection
alik604/cyber-security
Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities
Yimeng-Zhang/feature-engineering-and-feature-selection
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
anujdutt9/Feature-Selection-for-Machine-Learning
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
duxuhao/Feature-Selection
Features selector based on the self selected-algorithm, loss function and validation method
MIT-LCP/mimic-code
MIMIC Code Repository: Code shared by the research community for the MIMIC family of databases
dair-ai/ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
Towson-Research/GAN-Balancing-Datasets
COSC 490 Towson University
r7sy/IntrusionDetection
This repository contains a notebook implementing an autoencoder based approach for intrusion detection, the full documentation of the study will be available shortly.
CynthiaKoopman/Network-Intrusion-Detection
Machine Learning with the NSL-KDD dataset for Network Intrusion Detection
vinayakumarr/Network-Intrusion-Detection
Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
ylfeng250/KDD99CupDataSet-SVM
clean data ,feature selection , svm based kdd99
ati-ozgur/KDD99ReviewArticle
python and latex source files for reproducibility
afatcoder/LeetcodeTop
汇总各大互联网公司容易考察的高频leetcode题🔥
wmy1696/nowcoder-project
2019牛客网高级项目
Shauqi/Attack-and-Anomaly-Detection-in-IoT-Sensors-in-IoT-Sites-Using-Machine-Learning-Approaches
Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
kaiwaehner/ksql-udf-deep-learning-mqtt-iot
Deep Learning UDF for KSQL for Streaming Anomaly Detection of MQTT IoT Sensor Data
ychalier/anomaly
Classification anomaly detection in IOT with Machine Learning
doocs/leetcode
🔥LeetCode solutions in any programming language | 多种编程语言实现 LeetCode、《剑指 Offer（第 2 版）》、《程序员面试金典（第 6 版）》题解
elunez/eladmin
eladmin jpa 版本：项目基于 Spring Boot 2.6.4、 Jpa、 Spring Security、Redis、Vue的前后端分离的后台管理系统，项目采用分模块开发方式， 权限控制采用 RBAC，支持数据字典与数据权限管理，支持一键生成前后端代码，支持动态路由
wupeixuan/JDKSourceCode1.8
Jdk1.8源码解析
zq99299/note-book
新笔记本，java、git、elasticsearch、mycat、设计模式、gradle、vue， 等 。vuepress 构建的 Markdown 笔记。
Aruelius/wenshushu
基于 https://www.wenshushu.cn （文叔叔） 上传与下载文件的 Python 脚本
nibnait/algorithms
《剑指Offer》、LeetCode
wind-liang/leetcode
leetcode 顺序刷题，详细通俗题解，with JAVA
alibaba/p3c
Alibaba Java Coding Guidelines pmd implements and IDE plugin
v2ray/v2ray-core
A platform for building proxies to bypass network restrictions.
macrozheng/mall-admin-web
mall-admin-web是一个电商后台管理系统的前端项目，基于Vue+Element实现。 主要包括商品管理、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、设置等功能。
macrozheng/mall-learning
mall学习教程，架构、业务、技术要点全方位解析。mall项目（60k+star）是一套电商系统，使用现阶段主流技术实现。涵盖了SpringBoot、MyBatis、Elasticsearch、RabbitMQ、Redis、MongoDB、MySQL等技术，采用Docker容器化部署。