# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
However, these attacks have received less attention in the field of IoT network security. Figure 2a illustrates the growing focus of the research community on adversarial attacks. In contrast, Figure 2b highlights the low number of published research in the context of IoT ML-based security. In the field of traditional network security, the authors of [24] presented a survey of the current research landscape regarding the ML vulnerability to adversarial attacks. The survey reviewed different varieties of adversarial attacks encompassing evasion attacks and poisoning attacks and discussed their impact on various traditional network security ML-based models such as IDSs and MDSs. The study also outlined various defensive mechanisms that have been suggested to minimize the effects of adversarial attacks. How  
Figure 1: Generic process of adversarial attack. Figure 2: Total number of papers related to Adversarial Attacks published in recent years: (**a**) In all domains; (**b**) In the IoT domain only. The row data source is from [25] and it is completed based on our research findings in the IoT domain from 2019 to July 2023. The forecast was projected through quadratic curve modeling. ever, the survey's main focus was on traditional network security, while the security of IoT networks was very briefly discussed in a very short paragraph with a unique reference in the IoT context literature. [PERSON], H et al. [23] provided a comparative study of ML-based IDS vulnerability to adversarial attacks and paid more attention to the so-called shallow models (non-deep learning models). The authors assessed the resilience of seven shallow ML-based and one Deep Neural Network (DNN), against a variety of adversarial attacks commonly employed in state-of-the-art datasets using NSL-KDD [26] and UNSW-NB15 [27]. The survey paid minimal attention to adversarial attacks in the field of IoT security, offering only four references without any accompanying discussion. [PERSON] et al. [28] discussed adversarial black-box attacks against IDS and provided a survey of recent research on traditional network security and Software-defined Networking (SDN). Within its scope, the survey focused solely on reviewing research studies that employed adversarial generation attacks using different variants of Generative Adversarial Networks (GAN). Meanwhile, it overlooked the most widely used adversarial attack methods and defense strategies. Furthermore, limiting this survey to the black-box attacks was of interest, as it closely aligns with the most realistic circumstances for the adversary. However, studying the white-box attacks could be more interesting and beneficial for IDS's manufacturers who have complete access to their system and seek to assess its resilience against adversarial attacks, as well as in the scenario of insider attacks [29; 30], where the attackers can have access to sensitive resources and system information, the protection against white-box attacks can be more challenging. In the IoT network context, only a handful of published surveys have discussed adversarial attacks against ML-based security systems. For instance, in the survey in [30], the authors' primary focus was to review and categorize the existing body of information on adversarial attacks and defense techniques in IoT scholarly articles, with a unique emphasis on insider adversarial attacks. The authors presented a taxonomy of adversarial attacks, from an internal perspective, targeting ML-based systems in an IoT context. Additionally, they offered real-world application examples to illustrate this concept. The article also discussed defensive measures that can be used to resist these kinds of attacks in IoT. However, the external (black-box) adversarial attacks, which represent a realistic scenario, are not discussed, hence the Model Extraction attacks were not covered in the survey as the insider adversary usually has full knowledge of the ML model.