The Role of AI and ML in Preventing Cybercrime

According to a seminal Clark School study, a hacker attacks a computer with internet access every 39 seconds. 

What’s more, almost a third of all Americans have been harmed by a hacker at one point or another, and more than two-thirds of companies have been victims of web-based attacks. 

A 2020 IBM study showed that the total cost of data breaches worldwide amounted to $3.9 million, which just may sound the death knell for many businesses affected by breaches.

The Challenges That Come With Cyber Attacks

Challenges that come with cyber attacks

Cybersecurity professionals have to grapple with several challenges day-in and day-out.

Attacks Are Widespread

For starters, the good guys need to fend attacks from many different angles. 

After all, every organization will have hundreds, if not thousands, of devices, hooked online, presenting hackers with numerous potential attack vectors.

Since most companies face a shortage of skilled security professionals, IT security teams tend to be understaffed and outmatched by cyber attackers.

Think of it in terms of this analogy: you hire one or two security guards to protect a wide-open field. These guards need to prevent anyone from trespassing and plucking flowers from the field. 

That kind of challenge is easier said than done.

 

A Proactive Approach is Key

Historically speaking, security professionals have been more reactive than proactive. Hence, rather than preventing a problem from happening, many professionals spend their time resolving the attacks that have already occurred and mitigating their effect.

Proactive vs reactive

Even the professionals who do try to be proactive have a hard time. 

For instance, manual threat hunting is not only expensive but also time-consuming. 

This is not to mention that IT systems have become geographically distant, so both tracking incidents and hunting threats are made more difficult.

 

Cyber Attacks Keep Evolving

Cyber attackers keep evolving and advancing their methods of attack.

On the one hand, this can be blamed on the ever-evolving nature of technology. 

While the late 90s and early 2000s were marked by viruses, worms, and DDoS attacks, security professionals today have to contend with IoT attacks, Cloud attacks, and Machine learning and AI attacks.

On the other hand, hackers also keep elevating their game to outwit security professionals. 

It’s a classic arms race, where the good guys find new ways to stop the attackers, and the attackers figure out new ways to slip past the professionals. 

Some of the advanced attacks today include compromising digital certificates, stealing server keys, and exploiting weak crypto.

 

The Use of ML and AI in Cyber Security

Use of ML and AI in Cybersecurity

Because they are in a constant arms race, security professionals have had to develop new ways to ward off attackers.

This has included the use of artificial intelligence and machine learning systems.

In fact, security professionals today have the technology to train Machine Learning systems to systematically and autonomously gather data from all the attacks that affected the company.

Then, they can analyze said data, and find relations between the billions of signals present.

So, how have AI and ML changed the game?

 

1. Drawing Out the Lay of the Land

Armed with AI, security professionals are better able to gain a completely accurate inventory of all of the devices on a company’s network. 

They can also find out which users have access to these devices and which applications are open to them. 

It becomes easier to categorize these technological assets with respect to how critical they are for the business.

Moreover, a strong AI system can help security professionals understand the effectiveness of the many security tools at their disposal as well as the security protocols in place. 

The AI will highlight both the strengths and weaknesses of the infosec program.

 

2. Predicting Future Threats

If professionals want to spot any threats coming from a mile away, AI and ML can help with that.
Here’s how.

 

  • Recognizing and Learning From Patterns

AI systems are efficient at clustering data together and forensically analyzing them to find the methods of attack that worked in the past, along with what was compromised with each attack.

Also, security professionals rely on rule-based learning to teach the AI system which responses work best with each type of attack. 

Accordingly, once an attack happens, the system will be able to provide proper recommendations and assist with risk management.

Furthermore, AI can assist in creating security policies to help figure out which network connections are legitimate and which might seem suspicious. 

This can be integral in establishing a zero-trust model, especially since some attacks may rely on the fabrication of digital keys, which is similar to what Edward Snowden did.

 

  • Spotting New Threats Early On

Similar to fashion, hacking tends to follow trends. So, the types of attacks that are fashionable today might go out of style a few years in the future. 

With this in mind, security professionals can rely on AI systems to supply them with the latest knowledge of both global and industry-related cyber threats, which can inform how these professionals prioritize the digital assets most likely to get breached.

However, AI can also be a key tool in proactive security. 

Traning and Protection phase in AI and ML

For instance, AI can prove integral in vulnerability scanning and pentesting. It can help test for weaknesses and automate the process, saving professionals valuable time and acting as a much-needed helping hand.

An excellent case in point is Deep Exploit. 

Deep Exploit is an automated penetration tool that learns how to exploit a system. 

It does this by carrying out attacks either through brute force or methods it has learned. The tool targets all open ports yet can focus on a specific port number and application. 

Deep Exploit then learns from the feedback it receives on its successful attacks.

Another penetration testing solution is Pentoma, which looks at servers and applications to sniff out security risks, including, SQL Injection, cross-site scripting, and file inclusion. 

Pentoma relies on AI and ML to evolve and develop its techniques and assessments. 

The ability to spot new threats is becoming more and more critical as new technologies keep surfacing at an exponential rate and flooding the market. 

For example, the rise of IoT has forced security professionals to find new ways to protect these novel assets.

 

3. Behavior Analytics

One of the techniques of ML is called User and Event Behavioral Analytics, UEBA for short.

UEBA leverages machine and deep learning to identify the behavioral patterns of individuals using the corporate network and to build models of these users. 

The idea is to be able to spot any abnormal behavior and flag it as suspicious. 

However, while other statistical tools flag threatening behavior based on historical patterns, UEBA does not need predefined patterns or rules to notice that same behavior.

Varonis, a company specialized in data security and insider threat detection, offers solutions that utilize machine learning. 

Their software is capable of establishing a behavioral baseline for each user and keeping a watchful eye on the type of data they access. 

The system will recommend changing user permissions should a user no longer need access to certain data, be it because they haven’t accessed the data in a while or because their behavioral profile does not resemble that of other users of the same data.

 

4. Spotting Phishing Attempts and Similar Specific Attacks

Phishing attacks are a real problem.

When the pandemic struck, and thousands of businesses started operating from home, global mobile phishing attempts went up by 37%

To make matters worse, some cybercriminals used machine learning to enhance their attacks.

The good news is that machine learning can also be used to thwart phishing attempts. Machine Learning systems can spot threats in real-time, regardless of whether the device is online or offline.

This improves the security of mobile devices, making suitable IDs for them and substituting the need for passwords, which aren’t always safe.

 

Future of Cybersecurity

Cybersecurity is no longer just about protecting a company’s sensitive information. With the introduction of the GDPR and the CCPA, it has also become a legal matter.

Therefore, security and data governance are more important than ever.

As a result, AI and ML are bound to become more prominent figures in cybersecurity. They are critical in a world where security experts are already understaffed and crunched for time.

 

Published on Mar 2, 2021
Ashley Wilson
Written by Ashley Wilson
Ashley Wilson is a content creator, writing about business and tech. She has been known to reference movies in casual conversation and enjoys baking homemade treats for her husband and their two felines, Lady and Gaga. You can get in touch with Ashley via Twitter.

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