Machine learning is usually mentioned in contexts that actually refer to artificial intelligence or used as a synonym. Let us have a closer look at what the terms artificial intelligence, machine learning and deep learning (another common notion used in relation to AI) really mean. We will also discuss how we can use machine learning in cybersecurity.
Machine Learning in Cybersecurity – Definitions
As Encyclopaedia Britannica says, artificial intelligence represents “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.”
Machine learning differs from artificial intelligence in the sense that “machine learning is the method to train a computer to learn from its inputs but without explicit programming for every circumstance. Machine learning helps a computer to achieve artificial intelligence.”
Deep learning, the other term mentioned at the beginning of this article, represents “a set of Techniques for implementing machine learning that recognizes patterns of patterns – like image recognition. The systems identify primarily object edges, a structure, an object type, and then an object itself. “
Machine Learning in Cybersecurity – How Can It Help?
As Security Boulevard writes, “the role of Machine Learning in protecting people’s data in a digital world is growing all the time, and for good reason. Machine Learning is capable of constantly analyzing immense amounts of data in order to detect any kind of malware or virus that could indicate a security breach, then adjusting to protect against them.”
- Machine learning can detect malicious activity much faster and stop attacks before they even start.
- Machine learning can be applied to cybersecurity also by analyzing threats against mobile endpoints.
- Machine learning can enhance human analysis when it comes to detecting threats, analyzing the network and reducing alerts.
- Machine learning can help automate repetitive security tasks, which will significantly enhance the IT staff’s time for focusing on more important work.
- Machine learning could be used for closing zero-day vulnerabilities: “Some believe that machine learning could help close vulnerabilities, particularly zero-day threats and others that target largely unsecured IoT devices. There has been proactive work in this area: A team at Arizona State University used machine learning to monitor traffic on the dark web to identify data relating to zero-day exploits, according to Forbes. Armed with this type of insight, organizations could potentially close vulnerabilities and stop patch exploits before they result in a data breach. “
Machine Learning in Cybersecurity – Techniques
There are two main categories of machine learning algorithms, as can be seen in the following image:
What is an algorithm, first of all?
Also known as models, algorithms are mathematical expressions that represent data in the context of a business problem. The goal is to gain insights from this data.
In the case of supervised learning, “you know in advance what you want to teach a machine. This typically requires exposing the algorithm to a huge set of training data, letting the model examine the output, and adjusting the parameters until getting the desired results. You can then test the machine by letting it make predictions for a “validation data set”, or in other words, new unseen data.”
In the case of unsupervised learning, machines explore “a set of data. After the initial exploration, the machine tries to identify hidden patterns that connect different variables. This type of learning can help turn data into groups, based only on statistical properties. Unsupervised learning does not require training on large data sets, and so it is much faster and easier to deploy, compared to supervised learning.”
Among some of the most important machine learning methods, we mention dimensionality reduction, transfer learning, reinforcement learning and natural language processing.
With dimensionality reduction, the least important information is removed from a data set, in order to make the data set manageable: “The most popular dimensionality reduction method is Principal Component Analysis (PCA), which reduces the dimension of the feature space by finding new vectors that maximize the linear variation of the data. PCA can reduce the dimension of the data dramatically and without losing too much information […]“
Transfer learning implies the reusing of a “part of a previously trained neural net and adapting it to a new but similar task. Specifically, once you train a neural net using data for a task, you can transfer a fraction of the trained layers and combine them with a few new layers that you can train using the data of the new task. By adding a few layers, the new neural net can learn and adapt quickly to the new task.”
Reinforcement learning helps agents learn from experience. It implies the recording of actions in a set environment and the use of a trial-and-error approach. As Towards Data Science says, “you can use RL when you have little to no historical data about a problem because it doesn’t need information in advance (unlike traditional machine learning methods). In an RL framework, you learn from the data as you go.”
Natural language processing
Natural language processing (NLP) “deals with the interaction between computers and humans using the natural language” and can be found at the foundation of applications like Google Translate, Microsoft Word and Grammarly, Siri, Cortana, Alexa.
Machine Learning in Cybersecurity – Advantages and Disadvantages
When it comes to machine learning in cybersecurity, we have seen that it might be of great help to an industry that must always try to get ahead of its time and face the future. This does not mean that the use of machine learning in cybersecurity does not have any disadvantages. Let’s have a look!
- Machine learning can swiftly identify trends and patterns from large volumes of data. Moreover, it can pinpoint a causal relationship between events.
- Machine learning brings the advantage of automation. There will be less or no human interaction needed, because by giving the machines the ability to learn, we also give them the ability to make predictions and improve the algorithms on their own account.
- By learning from experience, machine learning algorithms continuously improve themselves and become more accurate and more efficient, which leads to making better decisions.
- Machine learning algorithms are also excellent at dealing with multi-dimensional and multi-variety data, even in dynamic or uncertain environments.
- Machine learning can be used for a wide range of applications, from healthcare to cybersecurity, for example. As Data Flair says, “where it does apply, it holds the capability to help deliver a much more personal experience to customers while also targeting the right customers.”
Machine learning needs massive amounts of data to train on, data which should be “inclusive/unbiased, and of good quality”.
Machine learning needs additional computer power requirements, but also enough time for algorithms to learn and develop.
The interpretation of data can sometimes be tricky too. It’s necessary to make sure that the right algorithms are selected.
Machine learning presents high error-susceptibility: “Suppose you train an algorithm with data sets small enough to not be inclusive. You end up with biased predictions coming from a biased training set. This leads to irrelevant advertisements being displayed to customers.”
Machine Learning in Cybersecurity – How Is It Used?
How do cybersecurity companies use machine learning nowadays?
- machine-learning algorithms are used for quickly spotting cyber threats
- for analysis of large amounts of data (like internal networks activity, known bad domains or suspected malware)
- for turning “data points into a behaviour map, which acts as a visual representation of a computer network and shows where threats could be coming in “
- for automating threat response capabilities
Machine Learning in Cybersecurity – How Heimdal™ Uses It?
Heimdal™ uses machine learning in one of our products’ basic technology, VectorN Detection, present in the Heimdal™ Threat Prevention solution for both endpoint and network?partner=Blog. Our solution focuses on detecting and preventing threats at a DNS level, representing another layer of security, besides the antivirus.
Machine learning algorithms are also used in our Next-Gen Antivirus, in the second stage of scanning and identifying even the most advanced threats. The stages are: Local File/Signature & Registry scanning, Real-Time Cloud Scanning, Sandbox and backdoor inspection, Process Behaviour-based scanning.
Machine Learning in Cybersecurity – Wrapping Up
As we have seen, machine learning can be a powerful tool for achieving exceptional cybersecurity, if used right. Thanks to automation, machine learning can increase the ability to respond to threats and save precious time (ergo, money) for the IT department.
However you choose to make use of machine learning in the cybersecurity domain, please remember that Heimdal™ Security always has your back and that our team is here to help you protect your home and your company and to create a cybersecurity culture to the benefit of anyone who wants to learn more about it.
Drop a line below if you have any comments, questions or suggestions – we are all ears and can’t wait to hear your opinion!