Enhancing Cybersecurity Resilience: A Study of Threat Detection and Mitigation Techniques in Modern Networks

Abstract

  As contemporary networks increase in complexity, so do the cybersecurity risks they encounter. This research investigates sophisticated threat detection and mitigation strategies, emphasizing the improvement of cybersecurity resilience in modern network settings. We assess existing tactics, including intrusion detection systems (IDS), artificial intelligence (AI)-driven solutions, and real-time anomaly detection, by assessing various threat vectors such as malware, insider attacks, and distributed denial-of-service (DDoS) assaults. The study underscores the necessity of proactive mitigation, cooperation among automated systems, and human supervision to guarantee effective security protocols. This study elucidates essential strategies for enhancing threat reaction times and reducing network vulnerabilities in a rapidly changing cyber environment through case analyses and performance indicators.