## 1 Introduction  
Cybersecurity is growingly considered a major concern for different computer applications. It needs to be noticed for all types of network traffic to ensure that any potential security issues are noticed and detected such as any kind of intrusions and attacks. Within IoT, the constraints of resources can affect the efficiency of the well-known security solutions making them luring targets for cyber-attacks. This reflects the necessity of tailored solutions to address the related-security issues in IoT networks. Recently, Machine Learning techniques have been increasingly adopted within several research fields. In particular, they have been integrated into IoT frameworks in order to enhance security and reinforce privacy. ML techniques have been gaining the upper hand because they relaxed the human intervention constraint through sophisticated algorithms that support decision-making tasks in a timely manner. In fact, ML-based models can analyze the traffic passed through devices and detect abnormal behaviors given the resource-constraint characteristic and the three main layers of IoT systems. ML techniques can be categorized into: (i) Supervised learning, (ii) Unsupervised learning, and (iii) Reinforcement learning approaches. In addition, they can also be grouped based on their architecture into (i) Traditional shallow learning and (ii) Deep learning techniques [1, 2]. One should note that the direct application of traditional ML techniques proved to be inefficient in handling data in an IoT environment [3, 4]. This can be inferred from several reasons related to ML algorithms in terms of complexity, scalability, real-time processing,data dimensionality, and data distribution. Firstly, ML algorithms introduce some complex challenges because of memory, computational complexity, and diversity of data types. Secondly, they are to some extent unable to provide scalable solutions, specifically for IoT devices due to energy constraints. Thirdly, ML algorithms are not designed to process large streams of data in real time. In other words, they typically assume training the entire data collection which is not fit for the IoT environment. Fourthly, their performance is considerably affected by the curse of dimensionality usually associated with real-world data. Lastly, the use of data out of the underlying data distribution is used for ML model training and testing in which attackers can craft adversarial examples and cause performance degradation [3; 4]. Although there is active research work to manage the aforementioned limitations, there is limited work to study the effect of AML on ML-based IoT solutions. These solutions' limitations are usually characterized by classification and detection issues where ML models are vulnerable to adversarial samples. The adversary can inject adversarial samples to affect the model decision boundary and therefore misclassify the analyzed data [3; 5]. Researchers have reported the potential vulnerability of ML models to adversarial attacks [1; 3; 4]. This has promoted researcher efforts related to Adversarial Machine Learning (AML) challenges. In fact, AML mainly concerns the intersection of computer security and machine learning fields. Specifically, it works on analyzing the attacks that aim to degrade the performance of ML-based models. It also investigates the process of generating and detecting the crafted adversarial examples and how eventually incorporate possible defensive mechanisms. The area has been extensively associated with applications dealing with images as primary data modality. On the other hand, it is still growing within the fields of network traffic analysis and IoT [6; 7]. There are several elements that reflect the crucial need to conduct the proposed survey. Those elements stem from the sacristy of studying the effect of AML attacks and defenses in the IoT context. As such, this survey focuses on investigating the current advances and trends of AML applied to the IoT-based IDS domain.