For a dynamic and powerful security platform, machine learning (ML)-based tools can be an essential element.
The technology can be used in a variety of tasks, such as detecting malware and network anomalies, categorizing user behavior, prioritizing vulnerabilities and threats, and also accurately predicting future attacks. Additionally, their use can help improve model risk, streamline threat classification – and even accurately predict immediate and potential attacks. In addition, ML-based automation relieves employees by minimizing manual effort. So ML has great potential for cybersecurity - but what should you pay attention to when implementing it in a corporate context? The experts from Palo Alto Networks provide an overview:
Supervised and Unsupervised Learning
The supervised learning methodology uses prepared data sets to help the algorithm distinguish between harmful and harmless data. After analyzing the input data with a specified target variable, it can create forecasts and make precise recommendations. It is the main type of ML. For example, supervised learning is used to classify threats: a solution can independently identify potential threats from the data sets if they have similar characteristics to the historical data.
With unsupervised learning, however, the algorithm independently explores the structure of the data without receiving target values that are known in advance. He then groups them (“clustering”). Unsupervised learning can provide cybersecurity teams with an overview of normal and abnormal behavior.
Generative AI (GenAI) expands the scope of machine learning by integrating both supervised and unsupervised learning. This technique leverages the data analysis and predictive ability of supervised learning, combined with the pattern recognition and exploratory nature of unsupervised learning. GenAI can be used primarily in areas such as source code interpretation, policy analysis, forensics or pentesting.
Data is the key
To ensure that ML algorithms execute correctly and produce the desired result, a large amount of high-quality data must be input. These data sets should represent the threats expected for each company so that the ML tool can learn the correct patterns and rules. They should also be up to date and constantly updated.
Data from different sources that do not interact well with each other and have gaps due to different data types or categorizations is difficult for a machine to evaluate. In order for the algorithm to develop its full potential, the data should always be complete, consistent and correct.
ML is predictive, not deterministic
ML deals with probabilities and outcome probabilities. That is, it uses provided data and past results to in turn predict potential results in the future. This makes ML predicative. Although the predictions are not deterministic, they are usually very accurate - and available much more quickly than after human analysis.
Rules for regression, classification, clustering and association
Depending on the type of problem to be solved, there are different methods of ML such as regression, clustering and association analysis. Regression aims to make a continuous output or prediction. In the field of cybersecurity, it can be used to detect fraud. Classification and clustering divide data into groups or categories, with clustering specifically grouping based on similarities in the data. During classification, the algorithm arranges or groups observations into predefined categories in order to be able to distinguish spam from harmless data.
Association rule learning uses previous experience with data to recommend a particular outcome much faster than a human would ever be able to. If an incident occurs on a website, solutions can be offered automatically.
ML and its limitations
ML algorithms are extremely efficient at pattern recognition and prediction making. However, they also require a lot of resources and are often quite error-prone because the data sets are limited in scope - so ML tools can also reach their limits.
Collaboration between humans and machines
To increase the performance of ML-based algorithms in cybersecurity, humans and machines must work together. While ML algorithms can perform data analysis, this does not replace the duty of cybersecurity teams to stay abreast of the latest technological breakthroughs and changes in the threat landscape.
Seamless integration and interaction with other tools
New ML techniques that are used in the cybersecurity environment can only unfold when they are seamlessly integrated into the process and technology landscape. For example, there is very little added value in identifying threats even more quickly if they can only be blocked or remedied after days. It is therefore crucial not to fall for the hype when it comes to ML, but rather to check in which areas the use of ML-based solutions actually makes sense.
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About Palo Alto Networks Palo Alto Networks, the global leader in cybersecurity solutions, is shaping the cloud-based future with technologies that transform the way people and businesses work. Our mission is to be the preferred cybersecurity partner and protect our digital way of life. We help you address the world's biggest security challenges with continuous innovation leveraging the latest breakthroughs in artificial intelligence, analytics, automation, and orchestration. By delivering an integrated platform and empowering a growing ecosystem of partners, we are the leaders in protecting tens of thousands of businesses across clouds, networks and mobile devices. Our vision is a world where every day is safer than the one before.
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