want1997 Goto Github PK

-

哈工大操作系统实验（蓝桥网站）

alienwar

assembly-study

assembly language study

bash-guide

A guide to learn bash

better

biancheng

biji

binary-grey-wolf-optimization-for-feature-selection

Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.

blasting_dictionary

爆破字典

books

编程随想的电子书 Github + IPFS 墙内镜像，每天同步

canet

CANET: An Effective CNN-Attention Model for Network Intrusion Detection

cnn-bigru-attention

CNN-BiGRU-Attention模型

cnn-lstm-bilstm-deepcnn-clstm-in-pytorch

In PyTorch Learing Neural Networks Likes CNN、BiLSTM

dbn-based-nids

An Intrusion Detection System based on Deep Belief Networks

developer-books

编程开发相关书籍整理分享，持续更新...

django-4-by-example

Django 4 by example, published by Packt

django-by-example-book

Code for the book Django By Example https://www.packtpub.com/web-development/django-example

efficient-cnn-bilstm-for-network-ids

evolopy

EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.

explainable-anomaly-detection-forindustrial-control-system-cybersecurity

exploit-exercises-protostar

Solutions for Exploit-Exercises Protostar

free-web-books

Learn books from TCP/IP | HTTP(s) | HTML、CSS、JS、JQuery | Vue | PHP | Web | Web Server

greywolfoptimization-mksvm

Using GreyWolfOptimization for feature selection and multi kernel SVM for classification for Malware Hunting on IoT devices

gwo-for-feature-selection

A model to select an optimal subset of features from the target data using swarm intelligence metaheuristic-based approach-Grey Wolf Optimization(GWO). A new variant of GWO was introduced by enhancing the exploration rate of GWO and then the variant was used to introduce the enhanced binary version.

how-to-ask-questions-the-smart-way

本文原文由知名 Hacker Eric S. Raymond 所撰寫，教你如何正確的提出技術問題並獲得你滿意的答案。

jiaoben

copy from https://github.com/cve-2020

learn_python3_spider

python爬虫教程系列、从0到1学习python爬虫，包括浏览器抓包，手机APP抓包，如 fiddler、mitmproxy，各种爬虫涉及的模块的使用，如：requests、beautifulSoup、selenium、appium、scrapy等，以及IP代理，验证码识别，Mysql，MongoDB数据库的python使用，多线程多进程爬虫的使用，css 爬虫加密逆向破解，JS爬虫逆向，分布式爬虫，爬虫项目实战实例等

linux011

Linux 0.11 版本源码

nids-based-on-dl-with-imbalanced-nsl-kdd

the DL methods used are : DNN, CNN, LSTM, GRU, CNN-LSTM, CNN-GRU, CNN-BiLSTM, CNN-BiGRU, the dataset used is : imbalanced NSL-KDD (KDDTrain+, KDDTest20+)

pat

浙大pat题目，js版，前端小伙伴们，算法刷起来，http://pat.zju.edu.cn/