Cyber Threat Analysis and Detection Using Advanced Deep Learning Models

Abstract

Cybersecurity threats have become increasingly sophisticated, posing significant challenges to organizations and governments.
Traditional threat detection systems often fail to detect novel and evolving cyberattacks. This study proposes a robust cyber threat detection system using Artificial Neural Networks (ANNs) based on event profiles. By analyzing behavioral patterns derived from network activities, the system effectively identifies anomalies indicative of malicious activities. Event profiles serve as comprehensive representations of network events, capturing both normal and abnormal behaviors.The proposed ANN model is trained on a labeled dataset consisting of diverse cyber threat scenarios. Advanced preprocessing techniques are applied to extract relevant features from event logs, enhancing the model's accuracy. Comparative analysis with conventional methods, including rule-based systems and signature-based detection, demonstrates the superiority of the ANN approach in detecting zero-day attacks and minimizing false positives.Experimental results show that the system achieves high detection accuracy, low false positive rates, and real-time threat identification. The findings underscore the potential of using neural networks in proactive cybersecurity defense mechanisms. This research paves the way for more resilient and adaptive threat detection systems, contributing to enhanced cyber resilience.Future work can explore the integration of ensemble learning methods and real-time adaptive models to further strengthen the detection capability. Additionally, the incorporation of explainable AI techniques will provide greater transparency and interpretability in cybersecurity decision-making.