IPv6 Subnetting – How to subnet IPv6?

by | Jul 15, 2021 | CCNA, Network Fundamentals

An IPv4 address is 32-bit long. Using these 32 bits, we can create 232 IP addresses. As the Internet is growing larger, 232 addresses are becoming less than enough. We need a larger address space for IP addresses. So, we need IPv6, which is a successor of IPv4.

An IPv6 address is 128-bit long. Using these 128 bits, we can create 2128 IPv6 addresses, which is more than enough for any practical scenario.

A 128-bit IPv6 address can be divided into three parts. The first 48 bits are used for network routing. The next 16 bits are used for subnetting and the last 64 bits are used for interface IDs.

When we want to create a subnet in IPv6, we use the 16 subnet bits. Using these 16 bits, we can create 216 = 65,536 subnets. This number of subnets is usually more than enough.

So, how to create a subnet mask using these 16 subnet bits? A 128-bit IPv6 address can be written as:

X1X2X3X4 : X5X6X7X8 : X9X10X11X12 : X13X14X15X16 : X17X18X19X20 : X21X22X23X24 : X25X26X27X28 : X29X30X31X32

Here, Xi is a hexadecimal digit. One hexadecimal digit is 4-bit long. And, hence, an IPv6 address is 32×4 = 128-bit long.

So, in these 128 bits, X1X2X3X4 : X5X6X7X8 : X9X10X11X12 are for network routing. X13X14X15X16 are for subnetting. And, X17X18X19X20 : X21X22X23X24 : X25X26X27X28 : X29X30X31X32  are for interface IDs.

Now, let’s say, we want to create four subnets. So, the binary mask for those 16 subnet bits will be 1100 0000 0000 0000, which is C000 in hexadecimal. So, X13X14X15X16 will be C000.

And, hence, the subnet mask will be:

FFFF : FFFF : FFFF : C000 : 0000 : 0000 : 0000

This is how we create a subnet mask for IPv6.

I hope this helps. However, interested readers who want to know more about how different web application attacks work and how we can prevent them can refer to the book “Web Application Vulnerabilities And Prevention.”

Facebooktwitterredditpinterestlinkedinmail

Calculate the pseudoinverse of a matrix using Python

What is the pseudoinverse of a matrix? We know that if A is a square matrix with full rank, then A-1 is said to be the inverse of A if the following condition holds: $latex AA^{-1}=A^{-1}A=I $ The pseudoinverse or the Moore-Penrose inverse of a matrix is a...

Cholesky decomposition using Python

What is Cholesky decomposition? A square matrix A is said to have Cholesky decomposition if it can be written as a product of a lower triangular matrix and its conjugate transpose. $latex A=LL^{*} $ If all the entries of A are real numbers, then the conjugate...

Tensor Hadamard Product using Python

In one of our previous articles, we already discussed what the Hadamard product in linear algebra is. We discussed that if A and B are two matrices of size mxn, then the Hadamard product of A and B is another mxn matrix C such that: $latex H_{i,j}=A_{i,j} \times...

Perform tensor addition and subtraction using Python

We can use numpy nd-array to create a tensor in Python. We can use the following Python code to perform tensor addition and subtraction. import numpy A = numpy.random.randint(low=1, high=10, size=(3, 3, 3)) B = numpy.random.randint(low=1, high=10, size=(3, 3, 3)) C =...

How to create a tensor using Python?

What is a tensor? A tensor is a generalization of vectors and matrices. It is easily understood as a multidimensional array. For example, in machine learning, we can organize data in an m-way array and refer it as a data tensor. Data related to images, sounds, movies,...

How to combine NumPy arrays using horizontal stack?

We can use the hstack() function from the numpy module to combine two or more NumPy arrays horizontally. For example, we can use the following Python code to combine three NumPy arrays horizontally. import numpy A = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) B =...

How to combine NumPy arrays using vertical stack?

Let’s say we have two or more NumPy arrays. We can combine these NumPy arrays vertically using the vstack() function from the numpy module. For example, we can use the following Python code to combine three NumPy arrays vertically. import numpy A = numpy.array([[1, 2,...

Singular Value Decomposition (SVD) using Python

What is Singular Value Decomposition (SVD)? Let A be an mxn rectangular matrix. Using Singular Value Decomposition (SVD), we can decompose the matrix A in the following way: $latex A_{m \times n}=U_{m \times m}S_{m \times n}V_{n \times n}^T $ Here, U is an mxm matrix....

Eigen decomposition of a square matrix using Python

Let A be a square matrix. Let’s say A has k eigenvalues λ1, λ2, ... λk. And the corresponding eigenvectors are X1, X2, ... Xk. $latex X_1=\begin{bmatrix} x_{11} \\ x_{21} \\ x_{31} \\ ... \\ x_{k1} \end{bmatrix} \\ X_2=\begin{bmatrix} x_{12} \\ x_{22} \\ x_{32} \\ ......

How to calculate eigenvalues and eigenvectors using Python?

In our previous article, we discussed what eigen values and eigenvectors of a square matrix are and how we can calculate the eigenvalues and eigenvectors of a square matrix mathematically. We discussed that if A is a square matrix, then $latex (A- \lambda I) \vec{u}=0...

Amrita Mitra

Author

Ms. Amrita Mitra is an author, who has authored the books “Cryptography And Public Key Infrastructure“, “Web Application Vulnerabilities And Prevention“, “A Guide To Cyber Security” and “Phishing: Detection, Analysis And Prevention“. She is also the founder of Asigosec Technologies, the company that owns The Security Buddy.

0 Comments

Submit a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Not a premium member yet?

Please follow the link below to buy The Security Buddy Premium Membership.

Featured Posts

Translate »