Getting Up to Date with AI-Based Malware

Ever since the topic of AI came into the public spotlight, people have noted the dangers that could come with such a technology. With enhanced learning capabilities beyond that of any human being that could ever exist, the ability to grow, manage and process data in ways we can’t even comprehend, and claims that AI could issue the dawn of a new superintelligence we just can’t compete with; it’s no wonder people are scared. However, while we hope to trust that developers of AI are doing the right thing, and so far everything seems to be okay with AI being introduced already too many industries, such as social media platforms and healthcare sectors, there’s one industry people should be worried about. Malware.

The cybersecurity industry is one of the most critical sectors of our time, both on an individual and corporate level. Of course, the aim of the game for cybersecurity protectors is always to stay one step ahead of the users, developers, and individuals/organisations with malicious intent in preventing what they create to hack and cause damage before it happens. When you add in the idea of AI-based malware, there’s no denying that things could get very complicated, very quickly, but is this something you need to be thinking about? Does it even concern you? Is this something we need to be scared of, or is it just a passing fad? Today, we’re going to explore it all.

It Started as AI-Protection

Several years ago, there was a list of US startup companies that took the idea of AI and machine learning to create protective tools and solutions that would help to protect systems and networks against malicious attacks. These are, in essence, security systems and preventatives that ran on algorithms. This seems like a good idea on paper. With all the security information we could feed into a machine learning system, including anti-spam data, all malware protection practices that have ever existed, a ton of intelligence data, and data and information based on every single cyberattack ever recorded (in theory), you could create a super security system that would be practically unbreakable.

Even the slightest indication of an attack could be detected and thwarted before any damage could be carried out. However, the idea is inherently flawed. Since nobody really knows how AI works or learns and it is really just a ‘black box’ that can make things happen, there’s literally no way you could tell whether a machine learning solution is even doing its job, or whether it’s doing nothing at all. That is, of course, until an attack happened and took something down, but in the area of data theft, there might be no sign the attack has even taken place. On the other hand, there’s no way you could really tell that an attacker has developed their own machine learning solution that’s even more effective that the protective machine learning solution. What gauge exists to see which system is better or more effective? There is none.

If an attack using a machine solution took place, this would put all security providers back to the beginning to code a new machine learning solution from scratch. “This is, of course, theory, because there’s no sign that AI has ever yet been used in a cyberattack, that we know of. Not that we even know that a machine learning attack would even leave signs the attack has happened until the data, money, or information, has already been taken,” ensures James Taylor, a project manager at Britstudent and 1day2write.

What Could a Machine Learning Attack Look Like?

By taking a moment to see what we should be looking out for, we can start to prepare for the somewhat inevitable case that machine learning technology will be used in a cyberattack. The first thing to consider is how attacks are carried out already. Hackers and attackers will use a mixture of techniques, including spam email, phishing practices, and creating content of disinformation, which is increasingly political, such as the content used during the Cambridge Analytica practices.

I mention this because machine learning is perhaps not going to be used to take down small eCommerce shops or steal the financial information from a small business. Of course, it could be, but the scale in which AI can operate would be far more effective at making its way onto a worldwide media publication or social media platform with millions of users and
then stealing information or uploading content to create the desired effect. It could affect everyone indirectly. So, taking the original forms of attacking into account, we can now apply the aspect of adding in a machine learning system full of big data harvested from bountiful sources.

By being able to also process data in real-time, a machine learning system could learn exponentially on what works and doesn’t work, thus creating content such as fake emails, social media posts, and phishing pages that become increasingly successful. In days, hours, or even mere minutes, a machine learning system could create millions of fake emails and attached phishing pages that could harvest data and personal information or spread content out to a very large percentage of the global population. The damage caused by an attack on this scale could be huge, and while there are plenty of people and organizations working tirelessly to prevent this from happening since we’re in a new era of technology, the threat is still very much real.

Other Ways a Machine Learning Attack Could Look Like

This isn’t the only way an attack could happen. Attackers could create a machine learning system that emulates that of security systems around the world, thus being able to test their attacks to see how successful they would be, all without having to carry out an attack first whatsoever. In this example, it’s simply a case of finding the best attack with the highest success rate and then applying it in a real-life scenario. “Alternatively, or in addition to the example above, machine learning solutions could churn out malware that works in countless different ways. Not only could attackers then test machine learning malware against fake machine learning security systems to see what works before using them in a real-world situation, but the process is limitless and has no boundaries,” explains Mary Harper, a business writer at Next Coursework and Write My X.

The more the machine learning solution learns about what works and what doesn’t work, the more successful it will become when creating newer ways to attack. This can all happen without any protective service realising, theoretically putting them years behind and unable to catch up.

A World Dominated By AI

There’s no denying that AI, in its current state, is very primitive, but there’s also no denying that it’s already extremely powerful. At the moment, the technology is so new that only a cluster of engineers relies on it, and even then, nobody really understands how it works. The real fear with AI that plagues every expert is the fact that once it takes off, the growth and development of the machine learning service is exponential, and there’s no way of knowing if it can be stopped. The true fear is with whether the AI to do it first will be one our side or the side of the attackers.