  The Internet of Things (IoT) paradigm has displayed tremendous growth in
recent years, resulting in innovations like Industry 4.0 and smart environments
that provide improvements to efficiency, management of assets and facilitate
intelligent decision making. However, these benefits are offset by considerable
cybersecurity concerns that arise due to inherent vulnerabilities, which hinder
IoT-based systems' Confidentiality, Integrity, and Availability. Security
vulnerabilities can be detected through the application of penetration testing,
and specifically, a subset of the information-gathering stage, known as
vulnerability identification. Yet, existing penetration testing solutions can
not discover zero-day vulnerabilities from IoT environments, due to the
diversity of generated data, hardware constraints, and environmental
complexity. Thus, it is imperative to develop effective penetration testing
solutions for the detection of vulnerabilities in smart IoT environments. In
this paper, we propose a deep learning-based penetration testing framework,
namely Long Short-Term Memory Recurrent Neural Network-Enabled Vulnerability
Identification (LSTM-EVI). We utilize this framework through a novel
cybersecurity-oriented testbed, which is a smart airport-based testbed
comprised of both physical and virtual elements. The framework was evaluated
using this testbed and on real-time data sources. Our results revealed that the
proposed framework achieves about 99% detection accuracy for scanning attacks,
outperforming other four peer techniques.
