Application of Deep Learning in Artificial Intelligence Systems for Cyberattack Identification and Prevention

Abstract

Cyberspace has been established as the fifth domain of warfare, alongside land, sea, air, and space. With the increasing complexity and frequency of cyberattacks, traditional security mechanisms are becoming increasingly inadequate, necessitating the integration of Deep Learning (DL) into cybersecurity. The capability of automated detection and prevention of cyber threats through the analysis of large volumes of data, anomaly identification, and attack prediction-threats that would otherwise remain undetected by conventional security systems-plays a crucial role in ensuring cyberspace security. Deep neural networks, such as Recurrent Neural Networks (RNNs), Long Short- Term Memory (LSTM) networks, and autoencoders, are instrumental in analyzing network traffic and user behaviour, enabling the early identification of cyberattacks. Deep learning can also be applied to automate incident response mechanisms. The high-speed analytical capabilities and self-adaptive nature of these models allow for dynamic cybersecurity defenses, including the autonomous blocking of malicious traffic, the identification of compromised systems, and the reduction of response time. Generative Adversarial Networks (GANs) further enhance security by simulating potential cyberattacks, thereby enabling the testing and refinement of defense strategies. However, these same networks can also be exploited for adversarial purposes, such as manipulating input data to deceive AI-driven cybersecurity systems, leading to incorrect classifications or attack predictions. Deep learning represents a transformative advancement in cybersecurity, offering intelligent, automated, and proactive solutions to combat evolving cyber threats. The continued development of adaptive AI-driven security systems will play a pivotal role in enhancing cyber resilience and safeguarding critical infrastructure against emerging cyber threats. The article examines the role of deep learning in cybersecurity, integrating technological aspects and the legal framework of the European Union (GDPR, eIDAS, AI Act) while analyzing the dual-use risk associated with generative adversarial networks (GANs). The emphasis is placed on the significance of biometric data and the necessity of human oversight, as excessive reliance on automated algorithms may lead to a false sense of security and discriminatory effects.