Introduction

Today’s most widely used security toolkit is OpenSSL, not only due to its licensing terms (including a commercial use with no restrictions whatsoever) but due to its rich plethora of facilities and building blocks we can use to build any sophisticated cryptosystem.

It is also a rich learning tool, and despite its serious nature, we can use it to understand several basic questions like how internet banking works or how cryptocurrencies function. You can also learn fingerprinting and blockchain logic using the Linux command line and OpenSSL utility.

Picture this - you are coming from a database background and getting into the world of IT monitoring or administration. While you are newly warming up to the Linux command line, you have to deal with Windows and Mac machines in your network. Add to that a bunch of Linux servers in your company’s data center.

As a systems administrator, how can you monitor each system’s health, disk space, and metrics? Unfortunately, learning the tools for each OS can be a drag. Many cloud companies offer their dashboard, and those can be helpful, but what about the physical machines in your network? How do you monitor them?

You will need some kind of instrumentation to monitor and take action based on the situation. With big data and high-speed networks and plenty of video-rich accesses, even the terabyte disks can fill up quickly, and you need to take stock of disk overruns, memory, CPU, and network usage. Or in the cybersecurity world, you need to monitor any suspicious activity on your company’s systems.

We talked about introductory OpenSSL in a previous blog Dipping Our Toes into OpenSSL, that covered how it supports rich cryptographic-centric operations, which are needed for all sorts of things in the security domain and even outside of it. Today, let's take the next step and understand some of the crypto arithmetic behind it, without making the topic too complicated.

Photo by Vanna Phon on Unsplash

Socat - the tool of choice for proxies and networking pipes

In prior blogs, our team has written about tools like netcat, Nmap, and Zeek that network security engineers widely use. Security analysts and threat hunters use these tools to help with their daily tasks. So this time let's talk about socat. Socat is the tool of choice if you are creating your own proxies or networking pipes.

To start a career as a security analyst, one must have a good understanding of the network and knowledge of networking tools. Let's begin with netcat.

What would life be without OpenSSL, can we even imagine one! 

A software application is a program or multiple programs that help end-users. Most applications use network resources, database resources, storage, and other cloud resources, to function. This connectedness is vital to keep in mind, not only how your end user may interact with the application, but also how vulnerable the application may be to malicious actors. One may use several different methods to protect the application, but a determined attacker with sufficient resources may access your application. So, how can we secure your home-grown IT applications? 

Python is an incredibly powerful programming language. It is not only for small school projects but instead, also used for Google AI in photo recognition and other monumental projects.

Introduction

Like it or not, most of us have a boss, and thus, we work under supervision. Our boss's job is to make sure we stay focused and complete our work. We have quotas to fulfill and projects to complete. They know what the desired and expected outcomes are, the same way data scientists understand the result they are trying to produce with supervised learning.

Supervised learning using Python

This blog is the third one of the series on learning Machine Learning using Python. In the first one, DataScience & Machine Learning: Where to start with Python, we covered setting up Python and installing the relevant libraries. In the second one Looking further into Machine Learning using Python, we covered different machine learning techniques and became familiar with supervised learning. We also talked about the scikit-learn toolkit and saw the SVM approach used due to its flexibility and usefulness.

Photo by Brooke Lark on Unsplash

Introduction

Welcome back! Earlier, we had covered the basics of getting started with machine learning and Python. (Here is that blog if you missed that: DataScience & Machine Learning: Where to start with Python) The current blog will take the next step and introduce some ML (Machine Learning) concepts and algorithms.

Since we are going to use Python, we will stick to the sklearn Python library as our choice.