Integrating sustainability into cybersecurity: insights from machine learning based topic modeling

Abstract

The increasing emphasis on sustainability in computing systems, ranging from small devices to large data centers, is fueled by environmental concerns. Moreover, ensuring cybersecurity in these interconnected networks demands the use of technologies such as resource-intensive cryptography and advanced intrusion detection systems. Our unique study investigates the integration of environmental sustainability into cybersecurity practices by identifying six pivotal themes through a textual analysis of related publications via machine learning based topic modeling. These themes highlight the convergence of cybersecurity with sustainable development goals (SDGs), particularly SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), and 8 (Decent Work and Economic Growth). These include the integration of sustainable cybersecurity measures in smart cities, sustainable digital protection strategies, the application of blockchain for cybersecurity in smart grids, and cybersecurity solutions for SMEs aimed at minimizing resource consumption. Additionally, the study explores multidisciplinary strategies and innovations across four perspectives: adaptive frameworks that prioritize resilience and environmental consciousness, policy shifts for coordinated protection, the power of AI in intelligent threat detection, and the impact of emerging technologies on both security and environmental efficiency. These strategies advocate for an evolved approach that incorporates advanced technologies such as AI, IoT, and blockchain into resilient, sustainable cybersecurity frameworks. This study not only provides a comprehensive overview of the intersection of cybersecurity and sustainability but also serves as a guide for future research and practical applications in creating robust, environmentally friendly cybersecurity practices.