Deep Learning-Based Architectures for Cybersecurity Threat Detection in Digital Ecosystems

Abstract

: As digital ecosystems grow increasingly complex and interconnected, cybersecurity threats have become more sophisticated, posing significant risks to data integrity, privacy, and system availability. Traditional signaturebased and rule-based intrusion detection systems (IDS) struggle to keep pace with the evolving landscape of cyberattacks. In response, deep learning (DL) techniques have emerged as powerful tools for cybersecurity threat detection due to their ability to automatically learn hierarchical features from large-scale data and detect unknown or zero-day attacks. This study investigates various deep learning architectures applied to cybersecurity threat detection, including convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), and autoencoders. We explore their efficacy in detecting anomalies, malware, network intrusions, and advanced persistent threats within diverse digital ecosystems, such as enterprise networks, cloud environments, and IoT infrastructures. We propose an integrated DL framework that combines CNN and LSTM layers to capture both spatial and temporal features of network traffic data. Using benchmark datasets like NSL-KDD and CICIDS2017, the model is trained and evaluated on detection accuracy, false positive rate, and computational efficiency. Our findings demonstrate that hybrid deep learning models outperform traditional machine learning and standalone DL models in accuracy and adaptability to new threat types. Autoencoders prove effective for unsupervised anomaly detection, while CNN-LSTM architectures excel in recognizing complex attack patterns over time. The study highlights challenges including the need for large labeled datasets, computational resources, and real-time deployment constraints. We discuss strategies for addressing these limitations, such as transfer learning and model compression. In conclusion, deep learning-based cybersecurity solutions present a promising avenue for enhancing threat detection capabilities in dynamic digital ecosystems, contributing to more resilient and proactive cyber defense mechanisms.