Harbi Yasmine, Medani Khedidja, Gherbi Chirihane, Aliouat Zibouda, Harous Saad
LRSD Laboratory, Ferhat Abbas University Setif-1, Setif 19000, Algeria.
Arabic Literature and Language Department, Mohamed Lamine Debaghine University Setif-2, Setif 19000, Algeria.
Sensors (Basel). 2024 Aug 9;24(16):5150. doi: 10.3390/s24165150.
Machine learning (ML) represents one of the main pillars of the current digital era, specifically in modern real-world applications. The Internet of Things (IoT) technology is foundational in developing advanced intelligent systems. The convergence of ML and IoT drives significant advancements across various domains, such as making IoT-based security systems smarter and more efficient. However, ML-based IoT systems are vulnerable to lurking attacks during the training and testing phases. An adversarial attack aims to corrupt the ML model's functionality by introducing perturbed inputs. Consequently, it can pose significant risks leading to devices' malfunction, services' interruption, and personal data misuse. This article examines the severity of adversarial attacks and accentuates the importance of designing secure and robust ML models in the IoT context. A comprehensive classification of adversarial machine learning (AML) is provided. Moreover, a systematic literature review of the latest research trends (from 2020 to 2024) of the intersection of AML and IoT-based security systems is presented. The results revealed the availability of various AML attack techniques, where the Fast Gradient Signed Method (FGSM) is the most employed. Several studies recommend the adversarial training technique to defend against such attacks. Finally, potential open issues and main research directions are highlighted for future consideration and enhancement.
机器学习（ML）是当前数字时代的主要支柱之一，尤其在现代实际应用中。物联网（IoT）技术是开发先进智能系统的基础。ML与IoT的融合推动了各个领域的重大进步，例如使基于物联网的安全系统更智能、更高效。然而，基于ML的物联网系统在训练和测试阶段容易受到潜在攻击。对抗性攻击旨在通过引入干扰输入来破坏ML模型的功能。因此，它可能带来重大风险，导致设备故障、服务中断和个人数据滥用。本文研究了对抗性攻击的严重性，并强调了在物联网环境中设计安全、健壮的ML模型的重要性。提供了对抗性机器学习（AML）的全面分类。此外，还对AML与基于物联网的安全系统交叉领域的最新研究趋势（2020年至2024年）进行了系统的文献综述。结果显示了各种AML攻击技术的存在，其中快速梯度符号法（FGSM）是使用最广泛的。多项研究推荐采用对抗性训练技术来抵御此类攻击。最后，强调了潜在的开放问题和主要研究方向，以供未来考虑和改进。
