DNA-RECON { Automatic Web Reconnaissance Tool }

Overview



  

ABOUT TOOL :

DNA-RECON is an automatic web reconnaissance tool written in python. This tool made for reconnaissance and information gathering with an emphasis on simplicity. Instead of executing several tools one after another it can provide similar results keeping dependencies small and simple.

For now, this tool uses API mode, which is based on hacker target API which makes it light and efficient and can be used for the identification of potential vulnerabilities. However, the API is the free one so the scans are limited to 100 calls per day per IP. So you can use a proxy to change your public IP after the API counter exceeds.

AVAILABLE ON :

  • Linux
  • Termux

TESTED ON :

  • Linux
  • Termux

REQUIREMENTS :

  • Internet
  • requests
  • colorama
  • ipapi
  • builtwith

FEATURES :

  • [+] Wizard interface !
  • [+] 10+Utilities !
  • [+] Easy for Beginners !
  • [+] All the information is extracted with APIs, no direct contact is made to the target !

INSTALLATION [Linux] :

  • git clone https://github.com/CYBERNIKUNJ/DNA-RECON
  • cd DNA-RECON
  • chmod +x *
  • ./install.sh
  • Just Type :- DNA-recon

INSTALLATION [Termux] :

  • git clone https://github.com/CYBERNIKUNJ/DNA-RECON
  • cd DNA-RECON
  • chmod +x *
  • ./install.sh
  • Just Type :- DNA-recon

SCREEN SHOTS [Termux]

WATCH VIDEO DEMONSTRATION

des

BUY ME A COFFEE :

Buy Me A Coffee

WARNING :

This tool is only for educational purpose. If you use this tool for other purposes except education we will not be responsible in such cases.

Owner
NIKUNJ BHATT
NIKUNJ BHATT
Example-custom-ml-block-keras - Custom Keras ML block example for Edge Impulse

Custom Keras ML block example for Edge Impulse This repository is an example on

Edge Impulse 8 Nov 02, 2022
MM1 and MMC Queue Simulation using python - Results and parameters in excel and csv files

implementation of MM1 and MMC Queue on randomly generated data and evaluate simulation results then compare with analytical results and draw a plot curve for them, simulate some integrals and compare

Mohamadreza Rezaei 1 Jan 19, 2022
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.13

Keon Lee 140 Dec 21, 2022
Extracts essential Mediapipe face landmarks and arranges them in a sequenced order.

simplified_mediapipe_face_landmarks Extracts essential Mediapipe face landmarks and arranges them in a sequenced order. The default 478 Mediapipe face

Irfan 13 Oct 04, 2022
Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

Streamlit Demo: The Udacity Self-driving Car Image Browser This project demonstrates the Udacity self-driving-car dataset and YOLO object detection in

Streamlit 992 Jan 04, 2023
Sky Computing: Accelerating Geo-distributed Computing in Federated Learning

Sky Computing Introduction Sky Computing is a load-balanced framework for federated learning model parallelism. It adaptively allocate model layers to

HPC-AI Tech 72 Dec 27, 2022
N-gram models- Unsmoothed, Laplace, Deleted Interpolation

N-gram models- Unsmoothed, Laplace, Deleted Interpolation

Ravika Nagpal 1 Jan 04, 2022
A style-based Quantum Generative Adversarial Network

Style-qGAN A style based Quantum Generative Adversarial Network (style-qGAN) model for Monte Carlo event generation. Tutorial We have prepared a noteb

9 Nov 24, 2022
codes for "Scheduled Sampling Based on Decoding Steps for Neural Machine Translation" (long paper of EMNLP-2022)

Scheduled Sampling Based on Decoding Steps for Neural Machine Translation (EMNLP-2021 main conference) Contents Overview Background Quick to Use Furth

Adaxry 13 Jul 25, 2022
🔪 Elimination based Lightweight Neural Net with Pretrained Weights

ELimNet ELimNet: Eliminating Layers in a Neural Network Pretrained with Large Dataset for Downstream Task Removed top layers from pretrained Efficient

snoop2head 4 Jul 12, 2022
Use tensorflow to implement a Deep Neural Network for real time lane detection

LaneNet-Lane-Detection Use tensorflow to implement a Deep Neural Network for real time lane detection mainly based on the IEEE IV conference paper "To

MaybeShewill-CV 1.9k Jan 08, 2023
Course content and resources for the AIAIART course.

AIAIART course This repo will house the notebooks used for the AIAIART course. Part 1 (first four lessons) ran via Discord in September/October 2021.

Jonathan Whitaker 492 Jan 06, 2023
Search Youtube Video and Get Video info

PyYouTube Get Video Data from YouTube link Installation pip install PyYouTube How to use it ? Get Videos Data from pyyoutube import Data yt = Data("ht

lokaman chendekar 35 Nov 25, 2022
Neighborhood Contrastive Learning for Novel Class Discovery

Neighborhood Contrastive Learning for Novel Class Discovery This repository contains the official implementation of our paper: Neighborhood Contrastiv

Zhun Zhong 56 Dec 09, 2022
A lightweight tool to get an AI Infrastructure Stack up in minutes not days.

K3ai will take care of setup K8s for You, deploy the AI tool of your choice and even run your code on it.

k3ai 105 Dec 04, 2022
Code for our NeurIPS 2021 paper: Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains

GateL0RD This is a lightweight PyTorch implementation of GateL0RD, our RNN presented in "Sparsely Changing Latent States for Prediction and Planning i

Autonomous Learning Group 16 Nov 03, 2022
Official implement of Paper:A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images

A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images 深度监督影像融合网络DSIFN用于高分辨率双时相遥感影像变化检测 Of

Chenxiao Zhang 135 Dec 19, 2022
To SMOTE, or not to SMOTE?

To SMOTE, or not to SMOTE? This package includes the code required to repeat the experiments in the paper and to analyze the results. To SMOTE, or not

Amazon Web Services 1 Jan 03, 2022
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation

PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation The paper: https://arxiv.org/abs/1704.03296 What makes

Jacob Gildenblat 322 Dec 17, 2022
(ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning"

CLNet (ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning" [project page] [paper] Citing CLNet If yo

Chen Zhao 22 Aug 26, 2022