TOWARDS SMARTER CYBER DEFENSE: LEVERAGING DEEP LEARNING FOR THREAT IDENTIFICATION AND PREVENTION

Abstract

The increasing sophistication of cyber threats has rendered traditional security measures inadequate, necessitating the adoption of deep learning-based techniques for enhanced threat detection and prevention. This study develops a Sequential Neural Network (SNN) model to improve cybersecurity defenses by identifying malicious activities with greater accuracy. The model is trained on the CERT Insider Threat v6.2 datasets, utilizing user activity modeling to detect anomalous behavior effectively. Performance evaluation reveals that the model achieved an accuracy of 67%, with precision, recall, and F1-score all at 0.67, indicating a balanced but moderate classification capability. The AUC-ROC score of 0.67 further suggests that while the model surpasses random classification, refinements are necessary for practical deployment. The confusion matrix analysis highlights challenges in distinguishing between certain cyber threats, resulting in misclassifications and false positives. Despite these challenges, the proposed deep learning approach demonstrates the potential of SNNs in cybersecurity by detecting complex attack patterns that traditional methods often fail to recognize. However, issues such as class imbalance, interpretability, and computational overhead must be addressed to improve model robustness. Future research will focus on enhancing model architectures, optimizing hyperparameters, and integrating explainable AI techniques to improve detection accuracy and reduce false positive rates. By leveraging deep learning, this study contributes to the development of smarter and more adaptive cybersecurity solutions, capable of responding to evolving threats in real time.