Deep Learning for Zero-Day Threat Detection and Mitigation
S Pavan Kumar Reddy, Umarani Nagavelli, Yelagori Sai Kiran, Chaitanya Sai Kondoju, Sathvika Bushmoni, Ananthula Yashaswi · 2024
This paper examines the profound capabilities of deep learning methods in tackling the crucial obstacles posed by zero-day threats in the field of cybersecurity. Zero-day attacks, known for their unforeseeable nature and originality, present substantial dangers to both companies and individuals, since they take use of vulnerabilities that presently lack any patch or security mechanism. This article provides a thorough examination of current methodology and technologies used to identify and mitigate zero-day attacks. It highlights the shortcomings of traditional approaches in dealing with the constantly changing nature of these threats. The proposed system utilizes deep learning to enhance anomaly detection, feature extraction, and behavioral analysis. This allows for the timely identification and response to new threats. This study utilizes deep neural networks, namely convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), to provide a strong basis for proactive threat mitigation and adaptive defensive mechanisms. The effectiveness and durability of the suggested deep learning method are shown across all types of threats through practical assessments and real-life examples. This highlights its potential to significantly transform the field of cybersecurity. Moreover, the paper explores the ethical aspects, difficulties, and prospects of incorporating deep learning technologies into the wider cybersecurity system. It highlights the significance of collaboration, transparency, and ongoing innovation in protecting digital resources and maintaining data integrity in an increasingly interconnected global environment.