<<< Detecting Cybersecurity Threats using Deep Learning

Abstract:

Cyber threats have emerged as a significant challenge for organizations worldwide, manifesting in diverse forms such as malware, phishing, and denial-of-service (DoS) attacks. These threats compromise sensitive information, disrupt critical operations, and continue to increase in both frequency and sophistication. Traditional detection mechanisms often struggle to adapt to evolving attack patterns, creating the need for more advanced and intelligent solutions. Deep learning models offer a promising approach by enabling the analysis of large-scale data and the identification of subtle, non-obvious patterns that human analysts might overlook.

This study proposes the design and implementation of a deep learning model for cyber threat detection using the BETH dataset, which simulates real-world log events. The dataset includes features such as process identifiers, thread activity, user IDs, argument counts, and return values, with a binary label (sus_label) denoting whether an event is malicious or benign. By leveraging this dataset, the model aims to proactively detect suspicious activities and enhance threat mitigation strategies. The outcomes of this work contribute to strengthening organizational cybersecurity measures, ensuring the protection of sensitive data, and maintaining operational resilience against emerging cyber threats.

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