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.

The IoT network is used to provide an object with connectivity to the Internet to allow the feedback of information. The IoT has the ambition to make objects interact with each other and with people. It promises to be the engine of great transformations in the lives of individuals by democratizing new uses and services in the mobility sector. This is why it is compared to the internet of the future. The global size of the IoT, the huge number of Things in the IoT, the changing topology of the IoT, and the resource constraints make routing in the IoT difficult. Routing is a necessary process in the Internet of Things because it enables the interchange of data between Things by quickly guiding and reliably delivering data through the network from its origins to its destinations. The Internet of Things is enabled by key pillars: radio frequency identification (RFID) and wireless sensor networks (WSNs) (IoT), NFC (Near Field Communication), BLE (Bluetooth Low-Energy technology), LoRa and Sigfox. This paper examines these different technologies as well as the barriers and challenges that must be solved.