June 2


The Role of Machine Learning in Cyber Security: A Beginner’s Guide

Data breaches uncovered more than 4.1 billion private records during the first half of 2019. Between 2005 and 2018, there were less than 9,000 recorded breaches. Whether or not your business falls into those stats, cybersecurity is becoming vital.

Machine learning is the solution to this growing problem. Why? Cybercriminals are leveraging bots in increasing numbers.

While you've probably heard of bots being used to spam Twitter with disinformation - they've found new applications. Cybercriminals use bots to flood victims with unwanted traffic (a.k.a. a DDoS attack). Phishing, impersonation, malware, and other hacking techniques are also now backed by AI.

As a result, cybersecurity in 2023 starts with data. Lots of it. And not just any data - complete, relevant, and rich in context. Without rich, structured data, machine learning defenses can't do their job.

This article is a beginner's guide to why machine learning is key to your cyber-protection.

Part One: The Danger of Machine Learning

Many new technologies can trace their origins back to either criminal activity or attempts to fight it. DNA storing information, video streaming, and other inventions have a dark past.

Fortunately, machine learning found its origin in checkers.

Today, machine learning is a foundational element of cybersecurity. The reason for this is because machine learning is behind many cyber-attacks - especially on companies.

Machine learning is critical in a few essential processes related to security. Pattern recognition, anomaly detection, natural language processing, and predictive analytics are all examples.

Machine learning can spot criminal patterns and unusual activity faster than humans. It can even convert unstructured text into intelligence. Using that intelligence, machines can identify patterns to make predictions.

Before we can understand how machine learning can solve cybersecurity weaknesses, we must understand how it creates them.

Phishing, Spear Phishing, and Good Old Spam

While the title sounds almost like a fishing tale, phishing is one of the biggest threats to companies today.

Hackers take advantage of mergers, acquisitions, and global pandemics using phishing. Cloud providers, supply chains, and two-factor authentication have all fallen to phishing.

Machine learning trains AI to create copycat simulations for phishing attempts. In case you forgot, phishing is a type of fraud. It is a disguise as a trustworthy source to collect passwords, usernames, and private data.

Machine learning can teach AI how to replicate automated emails. When you log in, change details or register somewhere - you are vulnerable to phishing. Machine learning is highly effective here because:

  • It is quick to adapt to changes
  • It is accurate and looks realistic
  • It can be triggered by actions you would expect to produce an email confirmation

Phishing can even replicate two-factor authentication requests. So, your secure login can trigger an email/text lookalike that scans your password. It then automatically uses that password to complete the actual authentication request.

Viola, machine learning, then bypasses one of the most secure log in or confirmation methods.

Spoofing and Impersonation

Spoofing and impersonation are replications of companies, brands, or persons. Typically, you might receive an email from your 'CEO' with terrible English. If you're tired or not paying attention, it could fool you.

Still, most phishing attempts aren't too convincing. But with machine learning, the AI learns how your boss writes. It impersonates his or her aphorisms and colloquial.

The email feels like your boss, but it isn't. The machine doesn't even need access to your boss's emails to do this. By scanning for social media posts or article excerpts, it can adapt those to an email.

Machine learning doesn't stop there. It can also replicate texts, videos, and even voice. Creepy? Yes. Dangerous? Definitely.

Sometimes the hacker actually breaks into the email account; other times, they create a similar one. The FBI calls this BEC (or Business Email Compromise).

Malware, Spyware, Trojans, and Ransomware

Many cyber-attacks use some form of the above to gain access to protected corporate documents. Malware is commonly spread by email using attachments or links.

Machine learning has given malware new life, however. There is now malware which can adapt to protection systems using evasion techniques. Malware can hide, cover its tracks, and disguise itself from specific security software.

There are many more forms of machine learning hacking, including captchas, password discovery, and social engineering. Still, these examples make the danger of machine learning clear. Companies' security and employees are now matched against AI.

Part Two: What is Machine Learning Cybersecurity?

The solution? To fight fire with fire. Machine learning is now critical to cybersecurity. Criminal methods are outmatching standard software and phishing checks.

If an AI bot can actually sound like your boss, something has to change. Companies can no longer expect their employees to outsmart phishing attempts that are so realistic.

So how do we compete?

Machine learning can salvage its morals from its foundation. Just like checkers, cybersecurity systems can analyze patterns to help learn from attacks. Machine learning allows us to follow their moves.

This doesn't make your systems impregnable (that doesn't exist), but it does make it intelligent. Not only that, but machine learning is cheaper, more effective, and more proactive. The keyword here is proactive.

The only catch? The quality of machine learning is based on the quality of the data it learns from. After all, machine learning is developing and manipulating patterns.

How Machine Learning Algorithms Use Data

Machine learning can use data in various ways, but one of the most common is to use ontologies.

You can think of ontologies like overlapping Venn diagrams. Ontologies are maps of correlations and accusations. Each area that sits between two different categories helps the machine understand the individual categories. 

Each overlap is a data point for an AI system. Where elements overlap, AI also understands the relationship between them. This is extremely useful for cybersecurity.

For example, ontologies allow machines to build a picture from data points. The machine first classifies data points that refer to certain entities. Then, the machine turns entities into events, which have their own classifications (e.g. the victim).

These links and classifications allow the machine to sort possible outcomes and make predictions.

How Do I Collect the Right Data?

Collecting the right data is all about the richness of the source. The more information you can feed your machine learning algorithm with, the better decisions it makes.

This is very unlike humans. If we are flooded with thousands of data points, our brains can't take all of it into account. As a result, we often end up making worse decisions than with fewer options.

Machine learning algorithms are the opposite. Like in criminal spoofing, machine learning performs better when it has access to more sources. The more examples it has of your boss's conversations, the better it can replicate them.

Cybersecurity works the same way. Businesses should source data from everything that happened. If data only focuses on threats, it will miss how those threats arose.

The threat may be a phishing email, but it was effective because malware collected email login details. Collect data on the applications, protocols, machines, networks, and sensors.

Using this data, you can stitch together a complete picture of the threat. These models can replicate different scenarios that algorithms can feed on. In response, AI can make decisions and build preemptive protections.

The Leadership Role

Leaders using machine-learning-based cybersecurity need to ask the right questions. Asking the right questions is often half the solution.

When it comes to data collection, focus on the kinds of data being collected. Does your team collect data that aids attack response? Teams will need information about where applications and data are used.

Additionally, you should ask if teams have data from these interaction points. Networks, endpoints, and clouds are good starting places.

Is your team leveraging the data they collected? A lot of data sits around unused or forgotten about. After a while, that data may be less and less relevant.

Make sure data is being structured to aid threat detection and decision making. If teams are leveraging data - is it effective?

Lastly, ask your teams if they feel confident in preventing attacks using that data. Detection is equally as important as the response.

Part Three: The Implementing Machine Learning Algorithms 

Using machine learning as a cybersecurity element requires structure. AI is not yet sophisticated enough to find and leverage its own data without guidance.

For machine learning to provide a security boost in your company, data needs to be organized and understandable. How do you make data understandable?

Understandable Data

Machine learning is intelligent. It can even recreate your voice for spoofing purposes. However, machine learning struggles when recording data from multiple sources.

Currently, machine learning cannot compute complex information from different sources together. You can think of this as a human language. Even if you are bilingual, it's still hard to understand two people speaking different languages simultaneously.

The data needs to be in the same 'language.' In other words, data should be compared and measured on the same metrics. That was algorithms can effectively use the data.


For maximum effect, integrate data and machine learning together. Machine learning should be tightly linked to the data it uses to make decisions.

Machine learning is a subset of AI in the sense that it learns rather than makes decisions. Machines learn by processing large amounts of data to make predictions and find anomalies.

AI then build upon that knowledge by representing it and constructing rules. While AI is important in cybersecurity, machine learning is what processes data.

Feeding machine learning algorithms with high-quality security data improves AI responses. This is especially important as the volume and complexity of attacks increases.

It is becoming impossible for humans to monitor and protect such large company systems. As companies scale, the number of vulnerabilities increases exponentially.

Human Analysis

While it may seem that once a robust machine learning system is set up, you can fire your IT team - that's not accurate. The job of your IT or cybersecurity consultants is changing.

In 2005, a chess tournament found unexpected strength in a certain human-machine combination. Weak chess players backed by a machine with good processes was the strongest performer. More so than a strong human player with weak machine processes - and certainly, more so than powerful computers alone.

From this tournament, we realized that the combination of average human intelligence with great computer processing power was king. Not just in the chess world, but also in cybersecurity.

Computers are great at specific things but lack creativity, intuition, or passion. Meanwhile, skilled humans can be outperformed only on processing power alone. Machine learning in cybersecurity functions in the same way.

The strongest defenses mash together human and bot intelligence. Machine learning can crunch thousands of possibilities a second. Yet humans are creative enough to find combinations of data that may spill valuable insight.

Wrapping Up

Machine learning is not enough to stop cybercrime on its own. But it is a necessary tool in defense of private records. Proactive cybersecurity is the only way to combat similar levels of attacks. 

Cybersecurity is becoming a game of data. Still, data is only as valuable as its analysis. Making sure your cybersecurity system integrates data is key to effective protection.

If you'd like to learn more about how cybersecurity companies utilize data to combat criminals - click here. Cybersecurity is an essential investment for any medium to a large company.

Protecting businesses against cyber-attacks that use machine learning requires innovation. Machine learning can help your business stay a step ahead of criminal behavior. Contact us to start building your defenses.

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