What changed in the cyber-security after COVID-19?

Area of Science:

• Cybersecurity
• Information Retrieval
• Machine Learning

Background:

• The COVID-19 pandemic significantly impacted various sectors, including cybersecurity.
• A large corpus of scholarly and non-scholarly documents from 2010-2021 was analyzed.
• Classical information retrieval techniques were employed to study the cybersecurity landscape.

Purpose of the Study:

• To examine the transition in cybersecurity discipline due to the COVID-19 pandemic.
• To identify key cybersecurity themes and trends before and after the pandemic.
• To understand the evolving nature of cybersecurity challenges.

Main Methods:

• Analysis of over twenty thousand documents using information retrieval techniques.
• Building topic models with the Latent Dirichlet Allocation (LDA) unsupervised machine learning algorithm.
• Categorizing the literature corpus by time period and peer-review status for trend analysis.

Main Results:

• Pre-COVID-19 cybersecurity topics included cyber threats, privacy policy, and blockchain.
• Post-COVID-19 cybersecurity themes show a shift towards healthcare privacy, cyber insurance, and supply chain risks.
• Persistent cybersecurity topics include malware and control system security.

Conclusions:

• The COVID-19 pandemic has demonstrably altered the focus of cybersecurity research and practice.
• Emerging trends highlight the need for tailored interventions in areas like healthcare data protection and supply chain security.
• Continuous monitoring of cybersecurity trends is crucial for adapting to new threats and challenges.