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.
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.
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
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.
In this blog series, we cover topics relevant to folks who are starting a career in cybersecurity. If you are one, you probably have had some exposure to cybersecurity best-practices in your personal life from a consumer perspective. Starting a cybersecurity career then challenges you to bridge your existing knowledge to your work life, and then expand it further to be an effective cybersecurity professional. In this first blog, we talk about some of the threats and challenges in today’s world that intertwine our personal and professional lives.
What does this have to do with my IT & Cybersecurity log analysis
Cyber threats are accelerating by leaps and bounds in frequency and sophistication. At the same time, the cybersecurity skills shortage is growing, a projected 1.8 million empty positions by 2022. Artificial Intelligence(AI) will have a pivotal role to play to halt these growing problems.
This article serves as a gentle introduction to natural language processing and how it makes life better.
In the world of cyber warfare, cybersecurity threats to businesses are accelerating by leaps and bounds. They are fast and often out pacing organizations ability to prepare. According to a early 2019 Internet Security Threat Report published by Symantec, a single misconfigured cloud infrastructure could result in loss of millions or could create a compliance nightmare for the organization such as that of GDPR – General Data Protection Regulation.