What is HSTS or HTTP Strict Transport Security?

by Amrita Mitra | Mar 8, 2017 | CCNA, CCNP, CompTIA, Data Breaches and Prevention, Encryption, End Point Protection, Exclusive Articles, Malware Prevention

What is HTTP Strict Transport Security (HSTS)?

HTTP Strict Transport Security or HSTS is a web server directive that informs a browser that the website should be accessed only using HTTPS and that HTTP should not be used.

Why do we need HTTP Strict Transport Security (HSTS)?

Let’s say a user is using public Wi-Fi. But unknowingly, he is connected to a rogue Access Point. Now, he types the HTTP URL of the website in his browser. And the web application redirects him to HTTPS. However, the attacker intercepts the HTTP connection and redirects the user to a fraudulent website, which may look similar to the actual website. At this point, if the user is unaware of the attack and provides any sensitive credentials to the fraudulent website, the sensitive data will directly reach the attacker. HTTP Strict Transport Security header informs a browser that the website should be accessed using HTTPS only.

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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...