Department of Information Technology, College of Computer, Qassim University, Qassim 51452, Saudi Arabia.
Faculty of Engineering and Information Technology, Taiz University, Taiz 6803, Yemen.
Sensors (Basel). 2023 Apr 7;23(8):3814. doi: 10.3390/s23083814.
The integration of the cloud and Internet of Things (IoT) technology has resulted in a significant rise in futuristic technology that ensures the long-term development of IoT applications, such as intelligent transportation, smart cities, smart healthcare, and other applications. The explosive growth of these technologies has contributed to a significant rise in threats with catastrophic and severe consequences. These consequences affect IoT adoption for both users and industry owners. Trust-based attacks are the primary selected weapon for malicious purposes in the IoT context, either through leveraging established vulnerabilities to act as trusted devices or by utilizing specific features of emerging technologies (i.e., heterogeneity, dynamic nature, and a large number of linked objects). Consequently, developing more efficient trust management techniques for IoT services has become urgent in this community. Trust management is regarded as a viable solution for IoT trust problems. Such a solution has been used in the last few years to improve security, aid decision-making processes, detect suspicious behavior, isolate suspicious objects, and redirect functionality to trusted zones. However, these solutions remain ineffective when dealing with large amounts of data and constantly changing behaviors. As a result, this paper proposes a dynamic trust-related attack detection model for IoT devices and services based on the deep long short-term memory (LSTM) technique. The proposed model aims to identify the untrusted entities in IoT services and isolate untrusted devices. The effectiveness of the proposed model is evaluated using different data samples with different sizes. The experimental results showed that the proposed model obtained a 99.87% and 99.76% accuracy and F-measure, respectively, in the normal situation, without considering trust-related attacks. Furthermore, the model effectively detected trust-related attacks, achieving a 99.28% and 99.28% accuracy and F-measure, respectively.
云与物联网 (IoT) 技术的融合带来了许多极具前景的技术，确保了物联网应用（如智能交通、智慧城市、智能医疗等）的长期发展。这些技术的爆炸式增长导致了威胁的显著增加，其后果可能具有灾难性和严重性。这些后果影响了用户和行业所有者对物联网的采用。基于信任的攻击是物联网环境中恶意行为的主要选择武器，这些行为可以利用已建立的漏洞作为可信设备，也可以利用新兴技术的特定功能（即异构性、动态性和大量链接对象）。因此，在这个领域中，为物联网服务开发更有效的信任管理技术已经变得紧迫。信任管理被认为是物联网信任问题的一种可行解决方案。在过去几年中，这种解决方案已被用于提高安全性、辅助决策过程、检测可疑行为、隔离可疑对象以及将功能重定向到可信区域。然而，当涉及到大量数据和不断变化的行为时，这些解决方案仍然不够有效。因此，本文提出了一种基于深度长短时记忆 (LSTM) 技术的物联网设备和服务的动态信任相关攻击检测模型。该模型旨在识别物联网服务中的不可信实体并隔离不可信设备。通过使用不同大小的不同数据样本评估所提出模型的有效性。实验结果表明，在所提出的模型在正常情况下（不考虑信任相关攻击），分别获得了 99.87%和 99.76%的准确率和 F 值。此外，该模型还能有效地检测信任相关攻击，分别获得了 99.28%和 99.28%的准确率和 F 值。
