A ssl analyzer which could analyzer target domain's certificate.

Last update: Dec 12, 2022

Related tags

Overview

ssl_analyzer

A ssl analyzer which could analyzer target domain's certificate.

Analyze the domain name ssl certificate information according to the input csv file (the csv file path can be entered in the configuration section), and write the detailed information into the mysql database. After the container starts, users can connect to mysql through the mysql client to view the analysis results (The startup demo video is shown below).

Get start

git clone https://github.com/vincentbin/ssl_analyzer.git
cd ssl_analyzer
docker-compose up --build

Configurations (optional)

analyzer.conf

[script]
# Use multi-thread or not
multi_thread=True
# The number of threads used by the task
thread_num=20
# The location of hosts csv file
hosts_csv_filename=./data/top-1m.csv
# The amount of hosts user want to analyze (Set to 0 to analyze all csv content)
analyze_num=120000

Database connection info

username: root
password: 123456

Running process is shown in the video below.

running_process1.5x.mp4

Code structure

.
├── analysis
│   ├── analysis_data.sql       // SQL statement for data analysis
│   ├── analysis_data_v2.ipynb  // Statistical analysis of the result data collected by the system
├── data
│   ├── cacert.pem              // Root certificate information
│   ├── certificate_table.csv   // Database storage results csv export file
│   ├── top-1m.csv              // Input domain name file (this file can be customized to specify the domain names you want to analyze)
├── db
│   ├── certificateTable.sql    // MySQL table design SQL for storing certificate details
│   ├── creatDB.sql             // Mysql build database SQL for container init
│   ├── init.sql                // Mysql container initialization SQL
│   ├── utf8mb4.cnf             // Mysql configuration settings
│   ├── Dockerfile              // Mysql Dockerfile
├── conf_reader.py              // Get analyzer.conf file's configuration information
├── crl_checker.py              // CRL check related code
├── db.py                       // Database related operation code
├── ssl_analyzer.py             // Code related to requesting domain name acquisition and parsing certificate information
├── requirements.txt            // Project dependencies
├── Dockerfile                  // Python script container
├── docker-compose.yml          // Configuration file for starting db & script containers
├── Dockerfile                  // Python script container
├── LICENSE                     // LICENSE
├── README                      // README

Our Analysis

Use 20 threads to execute and analyze 120,000 domain name information in the top-1m file concurrently.

Result

http://nbviewer.org/github/vincentbin/ssl_analyzer/blob/main/analysis/analysis_data_v2.ipynb

A ssl analyzer which could analyzer target domain's certificate.

Related tags

Overview

ssl_analyzer

Get start

Configurations (optional)

Database connection info

Running process is shown in the video below.

Code structure

Our Analysis

Result

Owner

vincent

Code for "Diversity can be Transferred: Output Diversification for White- and Black-box Attacks"

My implementation of transformers related papers for computer vision in pytorch

Pytorch ImageNet1k Loader with Bounding Boxes.

Using python and scikit-learn to make stock predictions

Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds

Pytorch implementation of our paper under review -- 1xN Pattern for Pruning Convolutional Neural Networks

MAg: a simple learning-based patient-level aggregation method for detecting microsatellite instability from whole-slide images

Behavioral "black-box" testing for recommender systems

Train Yolov4 using NBX-Jobs

Paaster is a secure by default end-to-end encrypted pastebin built with the objective of simplicity.

Official implementation of Densely connected normalizing flows

Official PyTorch Implementation of "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting".

The easiest tool for extracting radiomics features and training ML models on them.

Optical machine for senses sensing using speckle and deep learning

Source code of the paper "Deep Learning of Latent Variable Models for Industrial Process Monitoring".

JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation

An implementation of the methods presented in Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning

The implementation of the CVPR2021 paper "Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes"

Architecture Patterns with Python (TDD, DDD, EDM)