xiangliangCSDN

A-Lightweight-Firewall

基于 Linux 中的 Netfilter 框架，实现了一个轻量级状态检测防火墙，支持对报文进行状态检测和过滤，支持基于 IP 地址、端口、协议五元组的访问控制，支持基于 MAC 地址的访问控制，支持基于用户自定义策略的访问控制，支持封禁 PING 、 HTTP/HTTPS 等功能，支持设置和修改防火墙启用时间，支持查看和修改防火墙过滤规则，支持查看和记录防火墙日志文件。

aliyun-sign-action

all-of-frontend

你想知道的前端内容都在这

attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

CICIDS-2017-intrution-detection-

Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of reliable test and validation datasets, anomaly-based intrusion detection approaches are suffering from consistent and accurate performance evolutions. Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable. Some of these datasets suffer from the lack of traffic diversity and volumes, some do not cover the variety of known attacks, while others anonymize packet payload data, which cannot reflect the current trends. Some are also lacking feature set and metadata. CICIDS2017 dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs). It also includes the results of the network traffic analysis using CICFlowMeter with labeled flows based on the time stamp, source, and destination IPs, source and destination ports, protocols and attack (CSV files). Also available is the extracted features definition. Generating realistic background traffic was our top priority in building this dataset. We have used our proposed B-Profile system (Sharafaldin, et al. 2016) to profile the abstract behavior of human interactions and generates naturalistic benign background traffic. For this dataset, we built the abstract behaviour of 25 users based on the HTTP, HTTPS, FTP, SSH, and email protocols. The data capturing period started at 9 a.m., Monday, July 3, 2017 and ended at 5 p.m. on Friday July 7, 2017, for a total of 5 days. Monday is the normal day and only includes the benign traffic. The implemented attacks include Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet and DDoS. They have been executed both morning and afternoon on Tuesday, Wednesday, Thursday and Friday.

ClashForAndroid

A rule-based tunnel for Android.

clock-shop

🕙⏰🕰 Clock Shop is a website that collects beautiful clock codes

cnn-bilstm-attention

Multivariate Time Series Prediction using Keras (CNN BiLSTM Attention)

CNNOptimization

Using Particle Swarm Optimization (PSO) to Optimize a CNN (Convulsional Neural Network) - using an simple dataset (not using an image dataset)

Course-Project--Inverted-Pendulum-with-Fuzzy-Controller

Matlab simulation of inverted pendulum with fuzzy controller

xiangliangCSDN/CICIDS-2017-intrution-detection-

Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of reliable test and validation datasets, anomaly-based intrusion detection approaches are suffering from consistent and accurate performance evolutions. Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable. Some of these datasets suffer from the lack of traffic diversity and volumes, some do not cover the variety of known attacks, while others anonymize packet payload data, which cannot reflect the current trends. Some are also lacking feature set and metadata. CICIDS2017 dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs). It also includes the results of the network traffic analysis using CICFlowMeter with labeled flows based on the time stamp, source, and destination IPs, source and destination ports, protocols and attack (CSV files). Also available is the extracted features definition. Generating realistic background traffic was our top priority in building this dataset. We have used our proposed B-Profile system (Sharafaldin, et al. 2016) to profile the abstract behavior of human interactions and generates naturalistic benign background traffic. For this dataset, we built the abstract behaviour of 25 users based on the HTTP, HTTPS, FTP, SSH, and email protocols. The data capturing period started at 9 a.m., Monday, July 3, 2017 and ended at 5 p.m. on Friday July 7, 2017, for a total of 5 days. Monday is the normal day and only includes the benign traffic. The implemented attacks include Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet and DDoS. They have been executed both morning and afternoon on Tuesday, Wednesday, Thursday and Friday.

xiangliangCSDN/cnn-bilstm-attention

Multivariate Time Series Prediction using Keras (CNN BiLSTM Attention)

xiangliangCSDN/Course-Project--Inverted-Pendulum-with-Fuzzy-Controller

Matlab simulation of inverted pendulum with fuzzy controller

xiangliangCSDN/Daily-Interview-Question

我是依扬（木易杨），公众号「高级前端进阶」作者，每天搞定一道前端大厂面试题，祝大家天天进步，一年后会看到不一样的自己。

xiangliangCSDN/DaojiaRN

京东到家 React Native 项目。

xiangliangCSDN/DDos-detection-using-Autoencoders-

The Ddos dataset from Kaggle is used for building a K-means clustering and Autoencoder model that can classify and detect Ddos attacks

xiangliangCSDN/feature_extraction_ddos

xiangliangCSDN/GetTupleinfo

获取tcp，udp 五元组信息。

xiangliangCSDN/GRASSMARLIN

Provides situational awareness of Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) networks in support of network security assessments. #nsacyber

xiangliangCSDN/iptables_ipset_netfilter

防火墙组件iptables,ipset,netfiter note

xiangliangCSDN/isf

ISF(Industrial Exploitation Framework)，基于Python的工控漏洞利用框架

xiangliangCSDN/kdd99_feature_extractor

Utility for extraction of subset of KDD '99 features from realtime network traffic or .pcap file

xiangliangCSDN/NetfilterFirewall

基于Netfilter的轻量级防火墙，能够实现IP，端口和协议的五元组过滤。其中协议支持TCP,UDP和ICMP，支持掩码，能够设置配置文件并启动读取，能过添加日志。

xiangliangCSDN/network-data-visualization

针对网络流数据中的HTTP,DNS,IP五元组，邮件等类型数据进行可视化

xiangliangCSDN/Network-Traffic-Classification---Feature-Extraction

xiangliangCSDN/Pcap-Analyzer

Python编写的可视化的离线数据包分析器

xiangliangCSDN/psoCNN

Code to validate the "Particle swarm optimization of deep neural networks architectures for image classification" paper.

xiangliangCSDN/python-netfilter

python modules to manipulate iptables rules

xiangliangCSDN/rate-limit

用于限流的令牌桶算法，漏桶算法（Python实现）

xiangliangCSDN/snow-mall

商城init

xiangliangCSDN/synsanity

netfilter (iptables) target for high performance lockless SYN cookies for SYN flood mitigation

xiangliangCSDN/UAV

this repository contains some useful matlab simulınk files for uav sensors simulation , kalman filter propogation and autopilot implementation. equations and all mathematical model considered here is referred to this http://uavbook.byu.edu/doku.php book.

xiangliangCSDN/vue2-douban-market

一个vue全家桶入门Demo ！

xiangliangCSDN/vue2-manage

基于 vue + element-ui 的后台管理系统

xiangliangCSDN/vue3-jingdong

vue3开发京东到家