What is an IDS or Intrusion Detection System and how does it work?

by Amrita Mitra | Mar 2, 2017 | CCNA, CCNP, CompTIA, Data Breaches and Prevention, DoS and DDoS Prevention, End Point Protection, Information Security, Malware Prevention, Network Security, Security Fundamentals

What is an IDS or Intrusion Detection System?

An Intrusion Detection System or IDS is a device or software application that monitors network or system activities and sends alerts to system administrators at the proper time. IDS monitors both inbound and outbound traffic or activities to detect possible intrusions.

What are the different types of Intrusion Detection Systems?

IDS can be of two types:

Network Intrusion Detection Systems
Host Intrusion Detection Systems

What are Network Intrusion Detection Systems (NIDS)?

Network Intrusion Detection Systems, or NIDS, are placed at certain points within the network so that they can monitor all the traffic to and from all the devices of the network. It maintains a library of known attacks and continuously analyses the passing inbound and outbound traffic. If any traffic matches with that of the library of the known attacks or any abnormal behavior is sensed, an alert is sent to the administrator. Sometimes, NIDS is placed in the subnet near the firewall to detect if anyone is trying to break the firewall. NIDS can also compare signatures of similar packets and detect harmful packets that match any recorded signatures.

What are Host Intrusion Detection Systems (HIDS)?

Unlike NIDS, Host Intrusion Detection Systems or HIDS monitor individual hosts or devices in the network. It inspects all the inbound and outbound packets of the device and alerts the administrator on suspicious activities. It can take a snapshot of system files at certain intervals and match them to see if any critical file was modified or deleted. And it can alert the administrator when required.

How is an IDS different from a firewall?

A firewall monitors all inbound and outbound traffic of a network and detects possible intrusion attempts based on certain information like IP address, port number, certain protocols used, etc. Firewalls can block malicious traffic. However, IDS detects intrusions by comparing the traffic against signatures of malicious threats or by comparing system activities against a baseline of known behavior and alerts the system administrator if there is any intrusion attempt. Depending on the configuration, it can also prevent intrusions (IPS).

Pages: 1 2

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...