Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security

• Published in: IEEE access 2024, Vol.12, p.180597-180618
• Main Authors: Maaz, Muhammad, Ahmed, Ghufran, Sami Al-Shamayleh, Ahmad, Akhunzada, Adnan, Siddiqui, Shahbaz, Hussein Al-Ghushami, Abdulla
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Anomalies; Artificial neural networks; backdoor; Benchmarks; botnet; Computer security; Convolutional neural networks; Cybersecurity; Datasets; DDOS; Deep learning; deep learning (DL); Denial of service attacks; Ecosystems; Empowerment; Feature extraction; injection attacks; Internet of Things; Intrusion detection; IoT; Iron; Long short term memory; Machine learning; machine learning (ML); Multivariate analysis; Neural networks; Performance evaluation; Recurrent neural networks; Resilience; Robust control; Threat evaluation
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3482005

The Internet of Things (IoT) has dramatically changed human context with the environment, ensuring productivity, comfort, and quality of life through a variety of services and applications. Nevertheless, the rapid growth of IoT devices has introduced significant security concerns like device vulnerabilities, unauthorized access, and potential data breaches.This article deals with an immediate call to empower IoT resilience against a wide range of sophisticated and prevalent cybersecurity threats. We developed two novel hybrid deep learning mechanisms, CNN-GRU (Convolutional Gated Recurrent Neural Networks) and CNN-LSTM (Convolutional Long Short-Term Memory Neural Networks), and extensively evaluated their performance on the state-of-the-art Kitsune and TON-IoT publicly available datasets. These benchmark datasets contain a variety of multivariate IoT attacks. The aim is to demonstrate the robustness of the proposed algorithms in effectively identifying telnet, password, distributed denial of service (DDoS), injection, and backdoor vulnerabilities in IoT ecosystems. We achieved approximately 99.6% accuracy in correctly distinguishing between malevolent and non-malicious activities on the Kitsune dataset. Additionally, the TON-IoT dataset demonstrated a remarkable accuracy rate of 99.00%, with minimal drops and low false alert rates. The time efficiency of both proposed algorithms renders them well-suited for deployment in IoT ecosystems. We evaluated and cross validated the proposed techniques with current benchmarks. Consequently, the proposed hybrid deep learning anomaly detection approaches not only enhance IoT security but also provide a robust control system for addressing emerging multivariate cyber threats.