Deep Learning for Threat Detection and Analysis
Kiran Sree Pokkuluri, S. S. S. N. Usha Devi N., Alex Khang · Advances in information security, privacy, and ethics book series · 2025
Deep Learning has revolutionary potential to improve cybersecurity threat identification and analysis. This system quickly and accurately analyses large datasets by using Deep Learning, spotting patterns and anomalies that more conventional approaches would miss. This ability is essential for identifying sophisticated cyberthreats that traditional rule-based systems find difficult to detect, such as advanced persistent threats (APTs) and zero-day assaults. Deep learning models are constantly adapting to the trends in cybersecurity threats since they are taught on a variety of dynamic data sets. This flexibility lowers operational costs and speeds up response times by preserving the efficacy of cybersecurity measures without the need for frequent manual updates. Deep learning is also used to improve overall system reliability by lowering false positives, a typical cybersecurity concern and improve overall system reliability by lowering false positives, a typical cybersecurity concern.