Is Python based Machine learning Useful for Cyber security?

Programming language like Python is a suitable solution in the cyber security market. In the recent times, the role of Python for machine learning has acquired massive popularity. This has allowed data scientists, Python coders, ML engineers, and IT security experts to create a strong network of cyber intelligence systems that can protect modern digitalized ecosystems.

Let’s understand how Python machine learning courses are proving so useful in IT network security and cyber intelligence programs.

Cyber attacks have increased 300% since February 2020

The world of cyber security and threat management for IT companies has changed totally since the start of the COVID 19 pandemic. In the last 2 years, we have seen how big and small organizations have been targeted by cyber terrorists to extract money and ransom in the form of non-conventional currencies like crypto coins. With the growing demand to digitalize traditional IT systems, there is a lot of pressure on the IT teams and network administrators to not only find out vulnerabilities and gaps in the existing IT systems but also intelligently predict what’s going to happen if security measures are not up to the mark. We should know that no organization are100 percent safe from the cyber-attacks, and therefore, it is important for the industry leaders to build a strong foundation in IT security using reliable techniques. 

What is the biggest target for attacking teams?

Attackers are targeting from internal and external sources. In most cases, IT networking managers and security experts are able to find and fix internal threats. It’s the external attacks that put the companies at maximum risk. Machine learning is useful in predicting which attacking agents are more likely to target the external points of security concerns. 

IoT hijacks, personal information theft, phishing, and spamming are common and probably the costliest attacks you could think of. They cost billions of dollars to safeguard and retain ownership of. 

Healthcare data, financial data, marketing data, and IT customer data are potentially the highest form of data that attackers continuously keep an eye on, and whenever they find a loophole, they attack the system. Attackers are targeting every connected device you could possibly think of. While it’s great to adapt to the new class of connected devices, but leaving them unprotected and unsecured opens cans of worms that attackers like to exploit with a smile. 

According to leading researchers working with Python programming languages, it has been found that attackers exploit vulnerabilities linked to WiFi connections, hot spots, and Bluetooth access points. OEM manufacturers and device security software providers are utilizing Python for machine learning tools to automate the safety net to protect vulnerable devices. It has been particularly useful in securing devices connected to IT companies and remote locations. 

Why Python is the new AI favorite for security teams?

There are 100 billion data points in the world that could open up new security threats for attackers to take advantage of. These data points come from devices owned, managed, or serviced by some of the largest organizations, in public and private domains. These are largely owned by the government and military

You can consider every attack on these organizations as a form of cyber attack targeting the country or the region, and not necessarily the organization or industry. Therefore, AI engineers are trained with the intention to develop new technologies that can protect the interest of the country’s IT networks and military framework.

Large democracies like India, the US, France, Germany, Israel, etc spend billions of dollars on these security measures every year. If you are doing a project in security using Python Machine Learning techniques, you could be developing something in the context of Mobile app security, UPI payments security, Blockchain based cloud protection, and personal information security.

Why Python?

Well, firstly, despite its open source development legacy, Python retains security measures that ensure all its libraries are protected from external threats. Unless the programmers share it with the Python community, nobody from the outside is going to know the real science behind the working of the Python based security solution. Secondly, it is so simple to train with! Cyber security agents who may not necessarily have any previous experience with machine learning applications or their engineering can simply go to a Python coding platform and start using the extensive libraries to build a security framework with ML benefits.

Now, it takes 10-12 weeks to master Python for security solutions and implement them.