Best practices for segmentation of the corporate network of any company

Overview

Anurag's GitHub stats

Best-practice-for-network-segmentation

What is this?

This project was created to publish the best practices for segmentation of the corporate network of any company. In general, the schemes in this project are suitable for any company.

Where can I find diagrams?

Graphic diagrams are available in the Release page
The schema sources are located in the repository

Schematic symbols

Elements used in network diagrams:
Schematic symbols
Crossing the border of the rectangle means crossing the firewall.

Level 1 of network segmentation: basic segmentation

Level 1

Advantages

Basic segmentation to protect against basic targeted attacks that make it difficult for an attacker to advance on the network. Basic isolation of the productive environment from the corporate one.

Disadvantages

The default corporate network should be considered potentially compromised. Potentially compromised workstations of ordinary workers, as well as workstations of administrators, have basic and administrative access to the production network.

In this regard, the compromise of any workstation can theoretically lead to the exploitation of the following attack vector. An attacker compromises a workstation in the corporate network. Further, the attacker either elevates privileges in the corporate network or immediately attacks the production network with the rights that the attacker had previously obtained.

Attack vector protection:

Installation the maximum number of information protection tools, real time monitoring suspicious events and immediate response.
OR!
Segmentation according to level 2 requirements

Level 2 of network segmentation: adoption of basic security practices

Level 2

Advantages

More network segments in the corporate network.
Full duplication of the main supporting infrastructure for production network such as:

  1. mail relays;
  2. time servers;
  3. other services, if available.

Safer software development. Recommended implementing DevSecOps at least Level 1 of the DSOMM, what requires the introduction of a separate storage of secrets for passwords, tokens, cryptographic keys, logins, etc., additional servers for SAST, DAST, fuzzing, SCA and another DevSecOps tools. In case of problems in the supporting infrastructure in the corporate segment, this will not affect the production environment. It is a little harder for an attacker to compromise a production environment.
Or you can implement at least Level 2 of the SLSA.

Disadvantages

As a result, this leads to the following problems:

  1. increasing the cost of ownership and the cost of final services to customers;
  2. high complexity of maintenance.

If u like it?

Please subscribe - this is free support for the project image

Level 3 of network segmentation: high adoption of security practices

The company's management (CEO) understands the role of cybersecurity in the life of the company. Information security risk becomes one of the company's operational risks. Depending on the size of the company, the minimum size of an information security unit is 15-20 employees. Level 3

Advantages

Implementing security services such us:

  1. security operation center (SIEM, IRP, SOAR, SGRC);
  2. data leak prevention;
  3. phishing protection;
  4. sandbox;
  5. intrusion prevention system;
  6. vulnerability scanner;
  7. endpoint protection;
  8. web application firewall;
  9. backup server.

Disadvantages

High costs of information security tools and information security specialists

Level 4 of network segmentation: advanced deployment of security practices at scale

Each production and corporate services has its own networks: Tier I, Tier II, Tier III.

The production environment is accessed from isolated computers. Each isolated computer does not have:

  1. incoming accesses from anywhere except from remote corporate laptops via VPN;
  2. outgoing access to the corporate network:
    • no access to the mail service - the threat of spear phishing is not possible;
    • there is no access to internal sites and services - it is impossible to download a trojan from a compromised corporate networks.

🔥 Only one way to compromise an isolated computer is to compromise the production environment. As a result, a successful compromise of a computer, even by phishing, will prevent a hacker from gaining access to a production environment.

Implement other possible security services, such as:

  1. privileged access management;
  2. internal phishing training server;
  3. compliance server (configuration assessment).

Level 4

Advantages

Implementing security services such us:

  1. privileged access management;
  2. internal phishing training server;
  3. compliance server (configuration assessment);
  4. strong protection of your production environment from spear phishing.

🔥 Now the attacker will not be able to attack the production network, because now a potentially compromised workstation in the corporate network basically does not have network access to the production. Related problems:

  1. separate workstations for access to the production network - yes, now you will have 2 computers on your desktop.
  2. other LDAP catalog or Domain controller for production network;
  3. firewall analyzer, network equipment analyzer;
  4. netflow analyzer.

Disadvantages

Now you will have 2 computers on your desktop if you need access to production network. It hurts 😀

Support the project

Please subscribe - this is free support for the project

Have an idea for improvement?

You might also like...
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

DISCONTINUATION OF PROJECT. This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this

Let Python optimize the best stop loss and take profits for your TradingView strategy.

TradingView Machine Learning TradeView is a free and open source Trading View bot written in Python. It is designed to support all major exchanges. It

Using deep actor-critic model to learn best strategies in pair trading

Deep-Reinforcement-Learning-in-Stock-Trading Using deep actor-critic model to learn best strategies in pair trading Abstract Partially observed Markov

Code for
Code for "Learning the Best Pooling Strategy for Visual Semantic Embedding", CVPR 2021

Learning the Best Pooling Strategy for Visual Semantic Embedding Official PyTorch implementation of the paper Learning the Best Pooling Strategy for V

PyTorch implementation of the Value Iteration Networks (VIN) (NIPS '16 best paper)
PyTorch implementation of the Value Iteration Networks (VIN) (NIPS '16 best paper)

Value Iteration Networks in PyTorch Tamar, A., Wu, Y., Thomas, G., Levine, S., and Abbeel, P. Value Iteration Networks. Neural Information Processing

Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)
Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)

VIN: Value Iteration Networks A quick thank you A few others have released amazing related work which helped inspire and improve my own implementation

A best practice for tensorflow project template architecture.
A best practice for tensorflow project template architecture.

A best practice for tensorflow project template architecture.

Top #1 Submission code for the first https://alphamev.ai MEV competition with best AUC (0.9893) and MSE (0.0982).

alphamev-winning-submission Top #1 Submission code for the first alphamev MEV competition with best AUC (0.9893) and MSE (0.0982). The code won't run

Created as part of CS50 AI's coursework. This AI makes use of knowledge entailment to calculate the best probabilities to win Minesweeper.
Created as part of CS50 AI's coursework. This AI makes use of knowledge entailment to calculate the best probabilities to win Minesweeper.

Minesweeper-AI Created as part of CS50 AI's coursework. This AI makes use of knowledge entailment to calculate the best probabilities to win Minesweep

Comments
  • WSUS Server Terminology

    WSUS Server Terminology

    WSUS no longer uses the master/slave terminology. Instead use upstream & downstream servers.

    https://docs.microsoft.com/en-us/windows-server/administration/windows-server-update-services/plan/plan-your-wsus-deployment

    bug 
    opened by LinealJoe 2
  • Add Social preview

    Add Social preview

    Add Social preview Upload an image to customize your repository’s social media preview.

    Images should be at least 640×320px (1280×640px for best display). Download template

    enhancement 
    opened by sergiomarotco 1
  • [ImgBot] Optimize images

    [ImgBot] Optimize images

    Beep boop. Your images are optimized!

    Your image file size has been reduced by 9% 🎉

    Details

    | File | Before | After | Percent reduction | |:--|:--|:--|:--| | /Other/Powtoon_GIF.gif | 561.10kb | 507.21kb | 9.61% | | /Schematic symbols/Schematic symbols.jpg | 63.88kb | 61.17kb | 4.24% | | | | | | | Total : | 624.98kb | 568.38kb | 9.06% |


    📝 docs | :octocat: repo | 🙋🏾 issues | 🏪 marketplace

    ~Imgbot - Part of Optimole family

    opened by imgbot[bot] 0
  • Level 4 with one computer (Privileged Access Workstation)

    Level 4 with one computer (Privileged Access Workstation)

    Level four can be achieved with only one physical computer on your desktop. One can use virtual machines and call it a Privileged Access Workstation: https://techcommunity.microsoft.com/t5/data-center-security/privileged-access-workstation-paw/ba-p/372274

    It hurts a little less than two physical computers. ;)

    good first issue 
    opened by C0FFEEC0FFEE 7
Releases(4.1.3)
Owner
Security evangelist
RealTime Emotion Recognizer for Machine Learning Study Jam's demo

Emotion recognizer Table of contents Clone project Dataset Install dependencies Main program Demo 1. Clone project git clone https://github.com/GDSC20

Google Developer Student Club - UIT 1 Oct 05, 2021
TorchXRayVision: A library of chest X-ray datasets and models.

torchxrayvision A library for chest X-ray datasets and models. Including pre-trained models. ( 🎬 promo video about the project) Motivation: While the

Machine Learning and Medicine Lab 575 Jan 08, 2023
tree-math: mathematical operations for JAX pytrees

tree-math: mathematical operations for JAX pytrees tree-math makes it easy to implement numerical algorithms that work on JAX pytrees, such as iterati

Google 137 Dec 28, 2022
Malware Analysis Neural Network project.

MalanaNeuralNetwork Description Malware Analysis Neural Network project. Table of Contents Getting Started Requirements Installation Clone Set-Up VENV

2 Nov 13, 2021
Solving SMPL/MANO parameters from keypoint coordinates.

Minimal-IK A simple and naive inverse kinematics solver for MANO hand model, SMPL body model, and SMPL-H body+hand model. Briefly, given joint coordin

Yuxiao Zhou 305 Dec 30, 2022
Prefix-Tuning: Optimizing Continuous Prompts for Generation

Prefix Tuning Files: . ├── gpt2 # Code for GPT2 style autoregressive LM │ ├── train_e2e.py # high-level script

530 Jan 04, 2023
Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt) Task Training huge unsupervised deep neural networks yields to strong progress in

2 Aug 05, 2022
Public Models considered for emotion estimation from EEG

Emotion-EEG Set of models for emotion estimation from EEG. Composed by the combination of two deep-learing models learning together (RNN and CNN) with

Victor Delvigne 21 Dec 23, 2022
Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17

2017 VQA Challenge Winner (CVPR'17 Workshop) pytorch implementation of Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challeng

Mark Dong 166 Dec 11, 2022
Semi-supervised Transfer Learning for Image Rain Removal. In CVPR 2019.

Semi-supervised Transfer Learning for Image Rain Removal This package contains the Python implementation of "Semi-supervised Transfer Learning for Ima

Wei Wei 59 Dec 26, 2022
Differentiable Wavetable Synthesis

Differentiable Wavetable Synthesis

4 Feb 11, 2022
Attack on Confidence Estimation algorithm from the paper "Disrupting Deep Uncertainty Estimation Without Harming Accuracy"

Attack on Confidence Estimation (ACE) This repository is the official implementation of "Disrupting Deep Uncertainty Estimation Without Harming Accura

3 Mar 30, 2022
Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Seulki Park 70 Jan 03, 2023
[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation

Few-shot 3D Point Cloud Semantic Segmentation Created by Na Zhao from National University of Singapore Introduction This repository contains the PyTor

117 Dec 27, 2022
High-Resolution Image Synthesis with Latent Diffusion Models

Latent Diffusion Models arXiv | BibTeX High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach*, Andreas Blattmann*, Dominik Lorenz

CompVis Heidelberg 5.6k Dec 30, 2022
Towards End-to-end Video-based Eye Tracking

Towards End-to-end Video-based Eye Tracking The code accompanying our ECCV 2020 publication and dataset, EVE. Authors: Seonwook Park, Emre Aksan, Xuco

Seonwook Park 76 Dec 12, 2022
Semantic Image Synthesis with SPADE

Semantic Image Synthesis with SPADE New implementation available at imaginaire repository We have a reimplementation of the SPADE method that is more

NVIDIA Research Projects 7.3k Jan 07, 2023
MonoScene: Monocular 3D Semantic Scene Completion

MonoScene: Monocular 3D Semantic Scene Completion MonoScene: Monocular 3D Semantic Scene Completion] [arXiv + supp] | [Project page] Anh-Quan Cao, Rao

298 Jan 08, 2023
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

Dictionary Learning with Uniform Sparse Representations for Anomaly Detection Implementation of the Uniform DL Representation for AD algorithm describ

Paul Irofti 1 Nov 23, 2022
Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs

Continuous Query Decomposition This repository contains the official implementation for our ICLR 2021 (Oral) paper, Complex Query Answering with Neura

UCL Natural Language Processing 71 Dec 29, 2022