How AI has changed cyber security

We often hear about how artificial intelligence (AI) and machine learning will change our world. So where is cybersecurity headed?

27-07-2022 - 6 minute read. Posted in: cybercrime.

How AI has changed cyber security

How artificial intelligence (AI), machine learning and public digitalisation will change our world is something we hear about almost daily. So where is cybersecurity headed? If we examine today's trends, the answer is definitely: automation, automation and more automation.

What is artificial intelligence?

Artificial intelligence is the simulation of human intelligence in computers and machines. In other words, artificial intelligence seeks to mimic (or even advance) our problem-solving and decision-making abilities.

This process aims to automate certain tasks so that humans don't have to perform them constantly. For example, if you teach a machine to sort your email, you'll have more time for other projects

However, you can also build code that automatically spams other people's inboxes with ads or emails. On top of that, you can also program the code to learn how to bypass email filters. The best (or worst) part - you may not have to lift a finger afterwards to keep generating ad revenue. The code will more or less do it by itself.

Basically, AI is a double-edged sword. You can use it to automate almost any type of task. Whether it's good or bad depends entirely on your intentions.

Top 5 most interesting uses of AI in IT security

Similarly, people use AI in cyber security for different purposes. On the one hand, we have hackers using AI to improve their attacks. And on the other, IT professionals are using deep learning to build defenses against cyber threats.

Below, we have selected five of the most interesting AI applications in cybersecurity.

1. AI to create and fight phishing scams

Phishing attacks are some of the most damaging and costly cyber threats today. Hackers use them to persuade people to give away their credentials, financial and personal information.

For example, spear phishing scams use people's personal information to appear more authentic, so more people are likely to fall for them. They are some of the most commonly used phishing attacks, especially targeting organisations.

Today, hackers use AI and machine learning to automate these attacks. Most of them are carried out via email. And these emails are now filled with AI-powered malware that can fill your device with viruses or untraceable programs.

To stop these hackers, technical specialists have started using AI systems to combat phishing attacks.

Let's say you install an AI app on your computer. Over time, it will learn what kind of emails you receive, who you communicate with and the way you communicate. The AI will then look for inconsistencies. By detecting anomalies, AI can block any suspicious emails.

It also scans your inbox for:

  • Malware links
  • Fake login pages
  • Tracking Pixels
  • Virus-infected attachments
  • Forged signatures

2. Generative adversarial networks: how AI learns by competing against itself

A generative adversarial network (GAN) is AI built to learn without human supervision.

A GAN is based on a game in which two machine learning algorithms compete. One of them is called a generator, and the other - a discriminator.

The generator constantly simulates content, and the discriminator tries to spot its opponent's errors. You can think of it as a game between cops and cheats.

The police are trying to catch the scammers and ensure the use of legal money. On the other hand, the scammers try to make the fake money seem as real as possible.

The GAN strategy can also be used to improve cyber security. One AI simulates threats and attack vectors, while the other tries to spot them. In this way, you can protect yourself from threats that hackers haven't yet invented themselves.

3. Biometrics and AI to change passwords

Another thing that GANs are very useful for is image recognition. This has made it possible to combine artificial intelligence with biometrics and facial recognition.

Biometrics is a way of identifying people, based on human characteristics such as fingerprints, voice recognition and iris scans. And yes, digital devices use biometrics. A camera can record a person's facial features, while software processes the data. It's a very convenient way to identify a person.

Passwords are weak. We often hear how you should never use the same password for multiple services. But it's impossible to remember all your multiple passwords without the help of technology.

Biometrics can solve this problem. By looking into a camera and letting AI detect facial features, you can avoid having to enterenter login details. However, there is still some way to go before this technology can be used safely and properly.

The problem with current biometric technology is that hackers can easily exploit it and the systems themselves are very inaccurate.

However, AI and machine learning are producing promising results. And experts hope that biometrics will develop rapidly in the coming years. However, biometrics as identifiable data can raise many concerns and questions about our personal privacy.

4. Antivirus software uses AI to improve security

Antivirus systems have been used for many years now. Traditionally, antivirus developers used data signatures and files to look for patterns in threats in order to identify and stop them.

This method is sound and proven - it just works. But recently there has been a big increase in advanced malware and ransomware scams. As mentioned earlier, hackers have started using AI, among several tools, in their attacks.

For these reasons, traditional antivirus systems simply can't always keep up. Security tools like Avast and Windows Defender use AI and machine learning to improve their security.

They automate their detection and identification systems to provide increase security and validity of their applications. However, there is an even better way to improve antivirus software. It's called deep learning.

5. Deep learning to identify and predict cyber threats

Deep learning is a type of machine learning based on artificial neural networks.

Such a network system has several layers. First, the network receives raw data. This information moves between layers and keeps changing to allow the network to make predictions. Using this method, AIs can learn by processing data on their own.

Deep learning is a more complex version of machine learning that is constantly learning and can decide for itself whether its predictions are correct or not.

Cyber security systems like Deep Instinct use deep learning and cyber threats to learn. They are programmed to take raw data from malicious files and analyse it in so many ways that it would take a human an incredibly long time to do the same.

This results in systems that are constantly learning and can spot new viruses and malware in almost no time. They generally have higher detection rates and fewer false positives.

Remember to stay updated!

Cybersecurity and technological means continue to evolve and improve. The need for automation shows that our lives are becoming more complex. But it's important to remember that no technology is as good as the human who uses it. That's why we all need to stay alert to cyber threats in our daily lives, including phishing emails, pop-up ads and suspicious websites.

Author Sofie Meyer

Sofie Meyer

Sofie Meyer is a copywriter and phishing aficionado here at Moxso. She has a master´s degree in Danish and a great interest in cybercrime, which resulted in a master thesis project on phishing.

View all posts by Sofie Meyer

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