Towards Autonomous Cybersecurity: A Study on Self-Defending Networks and Their Role in Modern Threat Landscape
Keywords:
Advanced persistent threats (APTs), Automated incident response, Artificial intelligence (AI), Incident response automation, Machine learning (ML), Threat detection, Threat intelligence integration, User and entity behaviour analytics (UEBA)Abstract
The escalating complexity of cyber threats in the modern era demands a radical transformation in cybersecurity approaches. The traditional security measures, while partially effective, often fall short in adapting to the dynamic and sophisticated nature of contemporary attacks. To address these challenges, self-defending networks have emerged as a promising solution to bolster cybersecurity resilience. This study explores the core principles, methodologies, and significance of self-defending networks in the context of modern cybersecurity. By harnessing advanced artificial intelligence (AI) and machine learning (ML) technologies, self-defending networks proactively identify, assess, and mitigate cyber-risks in real-time. This proactive approach not only reduces incident response time but also enables them to stay ahead of emerging threats. Real-world case studies demonstrate the networks' efficacy in countering zero-day exploits, ransomware, and advanced persistent threats (APTs), underscoring their crucial role in safeguarding critical digital assets. While the self-defending networks exhibit significant promise, potential challenges and ethical considerations need to be addressed. As cyber-threats evolve, continuous research and development will be pivotal in enhancing the network's adaptability and effectiveness. The self-defending networks offer a compelling avenue to empower organizations in effectively defending against ever-evolving cyber risks, by reinforcing cybersecurity resilience in the modern threat landscape.
