Network Segmentation and VLAN

by Amrita Mitra | Mar 9, 2017 | CCNA, CCNP, CompTIA, Data Breaches and Prevention, Exclusive Articles, Information Security, Malware Prevention, Network Security, Security Fundamentals

unauthorized access of a part of the network, and thus increases the security of the sensitive part of the network.

Different Ways of Network Segmentation

A network can be segmented using bridges, routers, and switches. Let’s understand how that can be done.

Network Segmentation using Bridges

Bridging (Fig on Page 1 of the article) is a technology using which two or more local area networks that use the same protocols, like Ethernet or token ring, can be aggregated together. A bridge monitors each message on a LAN. It passes the messages that are destined within the same LAN and forwards those that are destined for a different interconnected LAN.

Bridges learn which addresses are in which network and develop a table, using which it decides whether a message should be forwarded to a different interconnected LAN. They work in layer 2 of the OSI reference model.

Advantages of network segmentation using bridges

Bridges can segment traffic in a network and thereby reduce the traffic seen in each sub-network, improving network response time. Using its buffering capabilities, it can also compensate for the speed discrepancies of two different networks.

Network Segmentation using Routers

Network Segmentation with Routers

When we need to aggregate two or more networks that use different protocols, we can use routers. A router can interconnect two or more networks and enable communication between them.

Routers function in layer 3 of the OSI reference model. They look at the destination IP address of each network packet passing through them and consult a table to determine to which network it should be forwarded. Routers can also implement broadcast filters and logical firewalls.

Advantages of network segmentation using routers

There are a number of advantages of using routers in segmenting a network :

Routers can interconnect two or more networks that use different protocols.
Routers can control broadcasts within the network.
Routers can filter inbound and outbound packets between LAN and WAN segments.
Routers can fragment large packets into smaller pieces and send them across the network, while bridges discard those.

Network Segmentation using Switches

Pages: 1 2 3 4

Calculate the pseudoinverse of a matrix using Python

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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?

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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?

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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?

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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?

by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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...