In today’s world, it is non-viable to work with cybersecurity technology without working closely with machine learning and artificial intelligence. Machine learning is a term used side by side with artificial intelligence and deep learning in most cases. We shall be learning what this “Machine Learning” is and how it can impact cybersecurity today.

What is Machine Learning?

Machine Learning, popularly shortened as ML, is an intelligence technique that assigns the learning task to a machine, using algorithms to detect patterns within a provided data bank. Deep learning, which is used in place of machine learning at times, is simply a type of machine learning. 

The idea of machine learning is for you to input a set of data and leave the machine or system to use this set of data and given algorithm to study patterns without the cont programming. The tasks of thinking, reading meaning to things, and learning from past experiences are all features related to intelligent beings; all these are the targets of systems and machines categorized under Machine Learning and Artificial Intelligence.

You might have seen “algorithm” being mentioned here and there without knowing what it is. An algorithm is a specific procedure for solving defined problems. You can refer to algorithms as steps used to bring solutions to a particular task.

The relationship between Machine Learning and Artificial Intelligence

As we have learned, machine learning is built on algorithms and is also designed to need an amount of computer or human supervision to solve problems.

AI (Artificial Intelligence) refers to the branch of computer science and technology that simulates computer systems to act like intelligent human beings. AI reduces the amount of human interaction and supervision by making specific choices based on pre-defined steps, i.e. algorithms that use machine learning to function. It is because of machine learning that the AI receives data at the time it is needed.

General Applications of Machine Learning in Everyday Life

• Helps to suggest similar interests in feeds of social media applications
• Helps in the assessment of tasks. For example, plagiarism checkers.
• Helps with electronic maps and even riding apps.
• Helps in the medical industry by detecting irregularities in data, explaining data, and even monitoring patients.

The role of Machine Learning in Cybersecurity.

Phishing Detection:

Phishing is one of the most common cyber threats in the world right now. It is also one of the oldest and most common ways to penetrate a network. Early detection of phishing attempts, maneuvers, and attacks is crucial for individuals and organizations. Machine learning can help with this early detection of phishing attacks. 

Phishing attempts can be divided into email phishing and website phishing. Machine learning can be used separately for these two types of phishing as the analysis content differs.

For an email, the header, attachment, and body (text) are analyzed for phishing attacks, while for a website, the URL (Uniform Resource Locator), images, and HTML (Hyper Text Mark-up Language) codes are analyzed.

This phishing detection involves the identification of spam emails and grouping them. NLP (Natural Language Processing) now has advanced features that scrutinize the body of an email and identify those with malicious aims.

Malware Detection:

The constant struggle against malware is very typical in the cybersecurity space. It is even more dangerous because malware affects not only computers and phones but also IoT devices. Machine learning helps with two types of analyses: static and dynamic.

The static analysis does not involve writing any code; it works by just analyzing a file. On the other hand, dynamic analysis involves launching the malware in a controlled environment to keep an eye on its activities. Static analysis works well with known malware, while dynamic analysis works especially well for polymorphic malware. ML also helps to combine these two analyses by combining supervised and unsupervised machine learning.

Penetration Testing:

Penetration testing is used to test for vulnerabilities. It is also known as ethical hacking and is defined as a technique companies use to test for vulnerabilities in their security systems. Experts use manual and automated pen-testing tools to fortify the security of applications and software.

Manual testing is mainly propelled by humans, while automated testing only reduces redundancy. In most cases, automation does not take care of the cognitive testing

aspect. However, integrating ML and AI into automated pen-testing brings cognitive automation into the picture, considerably reducing human interaction and supervision.

Challenges of Machine Learning

• Machine learning mostly requires a large amount of data for appropriate analysis.
• Correct data interpretation is impossible if suitable algorithms are not chosen.
• ML requires substantial computing power for algorithms to learn and develop.

Summary

Despite the challenges listed above,  machine learning is crucial to getting optimum cybersecurity. If harnessed properly, ML saves time, reduces effort and interaction from human beings, and even saves money.