Hot topics in cybersecurity research: insights from the Map of Science

Cybersecurity research is growing quickly, with more than 200,000 articles published in just the last few years. The sheer volume can make it impossible to figure out where to focus. Luckily, many of our ETO tools are designed to help. In our last post, we surveyed global cybersecurity research at a high level using ETO's Research Almanac. Today, we'll show how the Map of Science can help you hone in on critical pockets of cyber research around the world.

The Map of Science organizes hundreds of millions of global research publications into research clusters, which are groups of articles that cite each other often - typically because they share other things in common, such as topics, concepts, or languages. You can use the Map's built-in filters to narrow down to clusters that are relevant to a particular subject, or based on other characteristics, like growth rate, country, or organization.

Map of Science users often want to learn about the "top" areas of research on a particular subject. There could be many ways to define "top" research, of course, but ETO analysts often use a combination of the Map's growth and scale concepts to begin the analysis. With this approach, we look for research clusters that are both unusually large and have lots of recent articles - a rare pairing.

Let's see how this works for cybersecurity in particular. After tinkering with filters in the Map's list view, I decided to narrow down to research clusters with a growth rating of 75 or higher (that is, clusters with a higher proportion of recently published articles than 75% or more of all clusters in the Map) and with at least 400 new articles in the last five years. (You could experiment with different thresholds on each filter, if you liked.)

Out of nearly 86,000 clusters in the Map, only 1,448 clusters met these filters as of the publication of this post. I narrowed this set further by applying a filter for cybersecurity relevance, leaving 14 clusters in the end.

What are the clusters about? With the Map's key concepts feature and a few minutes spent browsing the top clusters' detail views, we can get a rough lay of the land:

We can immediately see several trends emerge from the list:

• Many of the clusters focus on security and privacy research related to other technologies that have made headlines in recent years. This includes AI, blockchain technology, Internet of Things (IoT) devices, electric vehicles, and facial recognition.
• The overlap between cybersecurity and AI is substantial, comprising all three of the top clusters and six of the fourteen in total. These clusters reflect both the use of AI for cybersecurity (e.g., network intrusion detection, spam detection) and the application of cybersecurity to AI to improve the security and privacy of those systems (e.g., privacy-preserving federated learning, membership inference attacks).
• Privacy and security research related to IoT devices (86152) and other connected systems, like electric vehicles (64170), remain important research areas. In addition, research on lightweight authentication protocols (21735) is closely related to these topics as these protocols are often used in edge computing devices such as those found in IoT devices, electric vehicles, and drones.
• Blockchain (77166) is a well researched topic in the field despite media attention on the topic waning over the last few years. This indicates that security and privacy applications that leverage blockchain technology—such as decentralized contracts, identity management, and supply chain security—may continue to hold promise.

As next steps, you might use the Map to identify key countries and organizations in these different areas of cybersecurity research - or run a similar "top clusters" analysis for subjects other than AI (how about chemistry, radiology, or robotics?).

As always, we're glad to help you get the most out of the Map of Science and our other resources. Visit our support hub to contact us, book live support with an ETO staff member or access the latest documentation for our tools and data. 🤖

This work was supported in part by the Alfred P. Sloan Foundation under Grant No. G-2023-22358.