The role of machine learning in cybersecurity

So, does that mean IT teams will become redundant soon, as AI-based security tools can do it all? Simply put, no. But for a more in-depth answer, we'll need to first understand what machine learning in cybersecurity is and what this technology holds for businesses in the future.

What is machine learning?

Machine learning refers to the ability of algorithms to learn patterns from existing data and use this knowledge to predict outcomes on new, previously unknown data without explicitly being programmed. The more information you feed to the machine learning engine, the more data it can analyze and, consequently, become more accurate.

But what does it mean to say that a machine is learning from the existing data? While traditional programming performs simple and predictable tasks by strictly following detailed instructions, machine learning allows the computer to teach itself through experience. In other words, it mimics human behavior in how to solve problems.

However, the fact that machine learning can improve itself isn't the only reason why it's so easy to find its models in the online wilderness. The sheer amount of information that businesses in different industries currently have to manage has become too vast for humans to tackle alone. As a result, companies rely on machine learning to process that data and quickly generate actionable insights.

For instance, an ML technique called a decision tree solves classification dilemmas and uses certain conditions or rules in the decision-making process. This particular technique is widely used in fintech (for loan approval and credit scoring) and marketing.

Machine learning solutions are also helpful for businesses in harvesting, organizing, and analyzing large volumes of customer data. This can include purchasing history or individual customer's typical behavior, such as online browsing habits. With such analyzed data, companies can then recommend relevant products tailored to their customers' preferences. Think Netflix: With an ML-driven model, it examines its users' histories on the platform to compile appropriate content recommendations for them to choose from. This increases the time users spend watching Netflix content and their overall satisfaction. Similarly, ML models pick up information relevant to the unique user on the Facebook feed and even moderate content on Instagram.

Machine learning can also boost a company’s cybersecurity by detecting and responding to threats faster than human analysts. This has led to the term “machine learning security,” which, while still a bit niche, describes how ML is used for security tasks like spotting malware or unusual network activity. With its ability to handle massive amounts of data, machine learning has become a key tool for keeping systems safe.

In addition, in most customer support self-service tools, users usually interact with a machine rather than a fellow human being. Such chatbots can answer basic questions and guide a person to relevant content on the website.

Lastly, even in the medical field, machine learning plays a huge role. These models can be trained to examine medical images or other information and then search for illness characteristics.

The importance of data quality in machine learning security

To get the most out of machine learning, you need to give it high-quality data. Think of it this way: ML can only analyze and learn from what you put into it, so if the data’s flawed, the insights will be too. This is especially critical for companies using ML to support decision making. Without quality data, ML models may lead to misguided decisions.

Alongside accuracy, machine learning security is also a vital part of data quality. Sensitive information should be prepared and protected before feeding it into ML models. Some ML platforms, while powerful, have vulnerabilities that could expose data if not managed carefully. In short, quality data should be both precise and secure.

Four types of machine learning

Machine learning traditionally has four broad subcategories that are defined by how the machine learns:

• Supervised machine learning models rely heavily on "teachers", meaning models that are trained with labeled data sets, which allow them to learn and become more accurate over time. For instance, if you want to teach the algorithm to identify cats, you'll have to feed it with pictures of cats and other things, all labeled by humans.

• Unsupervised machine learning looks for patterns and common elements in data. In turn, such machine learning can find similarities and trends that humans aren't explicitly looking for.

• Semi-supervised machine learning falls somewhere between supervised and unsupervised learning. In this case, the model is trained on a small amount of labeled data and lots of unlabeled data. Such a way of learning is beneficial when there's a lot of unlabeled data, and it's too difficult (or expensive) to label it all.

• Reinforcement machine learning is where an algorithm learns new tasks by interacting with a dynamic environment. Here, it is rewarded for correct actions, which it strives to maximize, and punished for incorrect ones. Such machine learning is widely used in cybersecurity, as it enables a broader range of cyber attack detection.

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Machine learning use cases in cybersecurity

As cybersecurity is a truly fast-paced environment where threats, technologies, and regulations constantly evolve, it’s the agility of machine learning that comes in handy.

ML-powered models can process massive amounts of data and, therefore, rapidly detect critical incidents. This means that machine learning enables organizations to detect various types of threats like malware, policy violations, or insider threats by constantly monitoring the network for anomalies. It is so because ML-driven algorithms learn to identify, for instance, new malicious files or activity based on the attributes and behaviors of previously detected malware.

In addition, using machine learning proves to be a good method for filtering your company's inbox from unsolicited, unwanted, and virus-infected spam emails, which may contain pernicious attachments such as malware or ransomware. For instance, the machine learning model used by Gmail not only sifts through spam but also generates new rules based on what it has learned in the past. ML methods, coupled with natural language processing techniques, can also detect phishing domains by picking on phishing domain characteristics and features that distinguish legitimate domains.

Last but not least, machine learning can significantly support online fraud detection and prevention. By using ML algorithms, companies can identify suspicious activities in transactional data. These algorithms are trained to recognize normal payment processes and flag suspicious ones. Also, ML-driven engines can be trained to spot when cybercriminals change their tactics as they automatically will retrain themselves to recognize a new fraud pattern.

These examples illustrate just a few use cases of machine learning in cybersecurity. But there are many others, such as vulnerability management, that can greatly impact business cybersecurity.

So, is it AI, machine learning, or deep learning?

Frequently, these terms – artificial intelligence, machine learning, and deep learning (DP) – are used interchangeably. We already defined machine learning, so now, let's see how it relates to artificial intelligence and deep learning.

Artificial intelligence, in the broadest sense, is a set of technologies that enable computers to perform various advanced tasks in a way similar to how humans solve problems. This makes machine learning a subfield of artificial intelligence.

In turn, deep learning is a subset of machine learning. It mimics the structure and functions of the human brain. Such systems use artificial neural networks that function like neurons in the brain. These neurons, also referred to as nodes, are used in chatbots or autonomous vehicles.

Difference between machine learning, artificial intelligence, deep learning, and cybersecurity

Even though machine learning brings some challenges when applied to cybersecurity (for instance, the difficulty in collecting large amounts of certain malware samples for the ML machine to learn from), it remains the most common approach and term used to describe AI applications in this industry.

In cases where shallow (or traditional machine learning) falls short, deep learning should be used. For example, when dealing with highly complex data such as images and unstructured text or when temporal dependencies have to be taken into account.

The future of machine learning in cybersecurity

In the current AI tool-filled climate, it’s easy to see how this technology can become better at specific tasks than we humans are. Luckily (or not), machine learning is not a panacea to all things cybersecurity. However, it provides and will continue to provide a great deal of support to cybersecurity or IT teams by reducing the load off of their shoulders.

Since many devices (like phones and laptops) connect to the company's networks daily, it is almost impossible for IT teams to monitor every single gadget. With AI-powered device profiling, you can improve the fingerprinting of endpoint devices and better understand the type and quantity of endpoints connecting to your network. This will help you create effective segmentation rules and stop unwanted devices (potentially including bad actors) from connecting.

Also, employing machine learning can improve your cybersecurity game by helping your IT team develop policy recommendations for security devices such as firewalls. In this case, machine learning learns what devices are connected to the network and what constitutes normal device behavior. In turn, ML-powered systems can make specific suggestions automatically — instead of your team manually navigating different conflicting access control lists for each device and network segment.

And so, integrating artificial intelligence in security, particularly through machine learning, can significantly enhance how your cybersecurity framework adapts to the evolving IT landscape. With more devices and threats coming online daily, the human resources available to tackle them are becoming scarce. In such an environment, machine learning can step in by helping sort out various complicated cybersecurity situations and scenarios at scale while maintaining constant surveillance 24/7.

Challenges of Machine Learning in Cybersecurity

Just like in life, the things that bring us the most value come with their own set of challenges. After all, you can’t expect great results without putting in some effort. The same goes for using machine learning in cybersecurity. It can be incredibly powerful, but getting the most out of it requires navigating a few obstacles along the way. So, here are a few challenges you might face when applying ML to data security:

• Adaptation to threats: Cyber threats are becoming increasingly intricate and complex, requiring ML models to undergo continuous retraining to identify new vulnerabilities effectively. This ongoing adaptation is essential to ensure that ML security systems remain capable of countering the latest tactics employed by hackers.

• Adversarial attacks (ML poisoning): By manipulating input data or introducing deceptive data, attackers can compromise an ML model’s effectiveness, reducing system reliability and jeopardizing operations by making it more difficult to accurately identify malicious activity.

• Operational issues: Integrating machine learning into an established cybersecurity framework isn’t always straightforward. There are a few challenges to consider, like the complexity of the implementation process, the risk of false positives that can add to analysts' workloads, regulatory compliance requirements, and the limited availability of professionals skilled in both ML and cybersecurity.

How does NordPass use machine learning?

Machine learning offers a wide range of applications for businesses, from applying it to cybersecurity to simply enhancing customer satisfaction. With artificial intelligence still making headlines, we're likely to see even more use cases in the future. However, machine learning in IT security will be one of the key areas that will continue to evolve.

NordPass is one of the companies that use machine learning. We do so to offer more accuracy and convenience for our customers. Our autofill engine relies heavily on machine learning to accurately categorize the field that it needs to fill in on a website or app – no matter if it is a sign-up, credit card, or personal information form. Remember those artificial neural networks? It has been trained using exactly those!

If you're interested in improving your IT team's online experience and enhancing overall company security, explore what enterprise password management can offer for your company.