Deep Learning Approaches for Malware Detection in Large-Scale Networks

• Author(s): Noah Ayanbode; Emmanuel Cadet; Edima David Etim; Iboro Akpan Essien; Joshua Oluwagbenga Ajayi
• Paper ID: 1710371
• Page: 483-502
• Published Date: 31-07-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 1 July-2019

Abstract

The increasing sophistication and volume of malicious software in large-scale network environments pose significant challenges for traditional security mechanisms, necessitating more adaptive and intelligent approaches. Deep learning (DL) has emerged as a promising paradigm for enhancing malware detection through its ability to automatically learn complex patterns from vast amounts of network and system data. This paper presents a comprehensive exploration of deep learning-based techniques for malware detection in large-scale networks, focusing on their architectures, feature extraction capabilities, and performance in real-world scenarios. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and hybrid deep architectures are examined for their strengths in capturing spatial-temporal dependencies, code semantics, and behavior signatures of malware. The study highlights how DL models leverage raw data sources such as network traffic flows, binary executables, and system call sequences, reducing dependence on manual feature engineering and improving detection accuracy against zero-day threats. Furthermore, we analyze the role of distributed and cloud-based DL frameworks in enabling scalable training and real-time inference, crucial for deployment in high-throughput network infrastructures. Case studies and benchmark results demonstrate that DL-based solutions consistently outperform conventional machine learning classifiers in detection rates, false positive reduction, and resilience to adversarial evasion techniques. However, challenges remain in terms of interpretability, computational overhead, model update strategies, and privacy-preserving data sharing across organizations. The paper concludes by outlining future research directions, including federated learning for collaborative detection, explainable AI for transparent decision-making, and the integration of DL with threat intelligence platforms to create adaptive, end-to-end security ecosystems. The findings underscore the transformative potential of deep learning in fortifying large-scale networks against evolving malware threats, while also emphasizing the need for balanced consideration of technical efficacy, scalability, and ethical implications.

Keywords

Deep learning, malware detection, large-scale networks, convolutional neural networks, recurrent neural networks, long short-term memory, hybrid deep architectures, zero-day threats, network traffic analysis, system call sequences, scalable inference, adversarial resilience, federated learning, explainable AI, threat intelligence integration.

Citations

IRE Journals:
Noah Ayanbode, Emmanuel Cadet, Edima David Etim, Iboro Akpan Essien, Joshua Oluwagbenga Ajayi "Deep Learning Approaches for Malware Detection in Large-Scale Networks" Iconic Research And Engineering Journals Volume 3 Issue 1 2019 Page 483-502

IEEE:
Noah Ayanbode, Emmanuel Cadet, Edima David Etim, Iboro Akpan Essien, Joshua Oluwagbenga Ajayi "Deep Learning Approaches for Malware Detection in Large-Scale Networks" Iconic Research And Engineering Journals, 3(1)