BBScan py3 - BBScan py3 With Python

Overview

BBScan_py3

This repository is forked from lijiejie/BBScan 1.5. I migrated the former python code to python3. The following description is the origin author's readme.

BBScan 是一个高并发漏洞扫描工具,可用于

  • 高危漏洞爆发后,编写简单插件或规则,进行全网扫描
  • 作为巡检组件,集成到已有漏洞扫描系统中

BBScan能够在1分钟内

  • 对超过2万个IP地址进行指定端口发现,同时,进行漏洞验证。例如,Samba MS17010漏洞
  • 对超过1000个网站进行HTTP服务发现(80/443),同时,请求某个指定URL,完成漏洞检测

BBScan is a super fast vulnerability scanner.

  • A class B network (65534 hosts) could be scanned within 4 minutes (ex. Detect Samba MS17010)
  • Up to find more than 1000 target's web services and meanwhile, detect the vulnerability associated with a specified URL within one minute

Install

pip3 install -r requirements.txt

开始使用

  • 使用1个或多个插件,扫描某个B段
python BBScan.py --scripts-only --script redis_unauthorized_access --host www.site.com --network 16

上述命令将使用 redis_unauthorized_access 插件,扫描 www.site.com/16,扫描过程将持续 2~4 分钟。

  • 使用1个或多个规则,扫描文件中的所有目标
python BBScan.py --no-scripts --rule git_and_svn --no-check404 --no-crawl -f iqiyi.txt

使用 git_and_svn 文件中的规则,扫描 iqiyi.txt 文件中的所有目标,每一行一个目标

--no-check404 指定不检查404状态码

--no-crawl 指定不抓取子目录

通过指定上述两个参数,可显著减少HTTP请求的数量。

参数说明

如何设定扫描目标

  --host [HOST [HOST ...]]
                        该参数可指定1个或多个域名/IP
  -f TargetFile         从文件中导入所有目标,目标以换行符分隔
  -d TargetDirectory    从文件夹导入所有.txt文件,文件中是换行符分隔的目标
  --network MASK        设置一个子网掩码(8 ~ 31),配合上面3个参数中任意一个。将扫描
  						Target/MASK 网络下面的所有IP

HTTP扫描

  --rule [RuleFileName [RuleFileName ...]]
                        扫描指定的1个或多个规则
  -n, --no-crawl        禁用页面抓取,不处理页面中的其他链接
  -nn, --no-check404    禁用404状态码检查
  --full                处理所有子目录。 /x/y/z/这样的链接,/x/ /x/y/也将被扫描

插件扫描

  --scripts-only        只启用插件扫描,禁用HTTP规则扫描
  --script [ScriptName [ScriptName ...]]
                        扫描指定1个或多个插件
  --no-scripts          禁用插件扫描

并发

  -p PROCESS            扫描进程数,默认30。建议设置 10 ~ 50之间
  -t THREADS            单个目标的扫描线程数, 默认3。建议设置 3 ~ 10之间

其他参数

  --timeout TIMEOUT     单个目标最大扫描时间(单位:分钟),默认10分钟
  -md                   输出markdown格式报告
  --save-ports PortsDataFile
                        将端口开放信息保存到文件 PortsDataFile,可以导入再次使用
  --debug               打印调试信息
  -nnn, --no-browser    不使用默认浏览器打开扫描报告
  -v                    show program's version number and exit

使用技巧

  • 如何把BBScan当做一个快速的端口扫描工具使用?

找到scripts/tools/port_scan.py,填入需要扫描的端口号列表。把文件移动到scripts下。执行

python BBScan.py --scripts-only --script port_scan --host www.baidu.com --network 16 --save-ports ports_80.txt

--save-ports 是一个非常有用的参数,可以将每次任务执行过程发现的端口,保存到文件中

  • 如何观察执行过程

请设置 --debug 参数,观察是否按照预期,执行插件,发起HTTP请求

  • 如何编写插件

请参考scripts文件夹下的插件内容。self参数是一个Scanner对象,可使用Scanner对象的任意方法、属性。

self.host self.port 是目标主机和端口

self.ports_open 是开放的端口列表,是所有插件共享的。 一般不在插件执行过程中再单独扫描端口

self.conn_pool 是HTTP连接池

self.http_request 可发起HTTP GET请求

self.index_headers self.index_status self.index_html_doc 是请求首页后返回的,一旦扫描器发现有插件依赖,会预先请求首页,保存下来,被所有插件公用

Owner
baiyunfei
我是一个执着的人,坚持做着自己热爱的事情!
baiyunfei
Delving into Localization Errors for Monocular 3D Object Detection, CVPR'2021

Delving into Localization Errors for Monocular 3D Detection By Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, Wanli Ouyang. Intr

XINZHU.MA 124 Jan 04, 2023
Categorical Depth Distribution Network for Monocular 3D Object Detection

CaDDN CaDDN is a monocular-based 3D object detection method. This repository is based off of [OpenPCDet]. Categorical Depth Distribution Network for M

Toronto Robotics and AI Laboratory 289 Jan 05, 2023
Deep Learning for 3D Point Clouds: A Survey (IEEE TPAMI, 2020)

🔥Deep Learning for 3D Point Clouds (IEEE TPAMI, 2020)

Qingyong 1.4k Jan 08, 2023
Explaining in Style: Training a GAN to explain a classifier in StyleSpace

Explaining in Style: Official TensorFlow Colab Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang, Yossi Gandelsman,

Google 197 Nov 08, 2022
Human4D Dataset tools for processing and visualization

HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media HUMAN4D constitutes a large and multimodal 4D dataset that contains a variet

tofis 15 Nov 09, 2022
Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

Zhengzhong Tu 5 Sep 16, 2022
An implementation for the ICCV 2021 paper Deep Permutation Equivariant Structure from Motion.

Deep Permutation Equivariant Structure from Motion Paper | Poster This repository contains an implementation for the ICCV 2021 paper Deep Permutation

72 Dec 27, 2022
A library for augmentation of a YOLO-formated dataset

YOLO Dataset Augmentation lib Инструкция по использованию этой библиотеки Запуск всех файлов осуществлять из консоли. GoogleCrawl_to_Dataset.py Это ск

Egor Orel 1 Dec 10, 2022
A dead simple python wrapper for darknet that works with OpenCV 4.1, CUDA 10.1

What Dead simple python wrapper for Yolo V3 using AlexyAB's darknet fork. Works with CUDA 10.1 and OpenCV 4.1 or later (I use OpenCV master as of Jun

Pliable Pixels 6 Jan 12, 2022
This is the source code for generating the ASL-Skeleton3D and ASL-Phono datasets. Check out the README.md for more details.

ASL-Skeleton3D and ASL-Phono Datasets Generator The ASL-Skeleton3D contains a representation based on mapping into the three-dimensional space the coo

Cleison Amorim 5 Nov 20, 2022
The repository for our EMNLP 2021 paper "Finnish Dialect Identification: The Effect of Audio and Text"

Finnish Dialect Identification The repository for our EMNLP 2021 paper "Finnish Dialect Identification: The Effect of Audio and Text". We present a te

Rootroo Ltd 2 Dec 25, 2021
An official implementation of "Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation" (ICCV 2021) in PyTorch.

Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation This is an official implementation of the paper "Exploiting a Joint

CV Lab @ Yonsei University 35 Oct 26, 2022
Mercer Gaussian Process (MGP) and Fourier Gaussian Process (FGP) Regression

Mercer Gaussian Process (MGP) and Fourier Gaussian Process (FGP) Regression We provide the code used in our paper "How Good are Low-Rank Approximation

Aristeidis (Ares) Panos 0 Dec 13, 2021
A PyTorch implementation of Radio Transformer Networks from the paper "An Introduction to Deep Learning for the Physical Layer".

An Introduction to Deep Learning for the Physical Layer An usable PyTorch implementation of the noisy autoencoder infrastructure in the paper "An Intr

Gram.AI 120 Nov 21, 2022
Accelerated NLP pipelines for fast inference on CPU and GPU. Built with Transformers, Optimum and ONNX Runtime.

Optimum Transformers Accelerated NLP pipelines for fast inference 🚀 on CPU and GPU. Built with 🤗 Transformers, Optimum and ONNX runtime. Installatio

Aleksey Korshuk 115 Dec 16, 2022
🤗 Push your spaCy pipelines to the Hugging Face Hub

spacy-huggingface-hub: Push your spaCy pipelines to the Hugging Face Hub This package provides a CLI command for uploading any trained spaCy pipeline

Explosion 30 Oct 09, 2022
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Simple and Robust Loss Design for Multi-Label Learning with Missing Labels Official PyTorch Implementation of the paper Simple and Robust Loss Design

Xinyu Huang 28 Oct 27, 2022
PyTorch module to use OpenFace's nn4.small2.v1.t7 model

OpenFace for Pytorch Disclaimer: This codes require the input face-images that are aligned and cropped in the same way of the original OpenFace. * I m

Pete Tae-hoon Kim 176 Dec 12, 2022
天勤量化开发包, 期货量化, 实时行情/历史数据/实盘交易

TqSdk 天勤量化交易策略程序开发包 TqSdk 是一个由信易科技发起并贡献主要代码的开源 python 库. 依托快期多年积累成熟的交易及行情服务器体系, TqSdk 支持用户使用极少的代码量构建各种类型的量化交易策略程序, 并提供包含期货、期权、股票的 历史数据-实时数据-开发调试-策略回测-

信易科技 2.8k Dec 30, 2022
Implementation of DropLoss for Long-Tail Instance Segmentation in Pytorch

[AAAI 2021]DropLoss for Long-Tail Instance Segmentation [AAAI 2021] DropLoss for Long-Tail Instance Segmentation Ting-I Hsieh*, Esther Robb*, Hwann-Tz

Tim 37 Dec 02, 2022