# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
In [31], the authors surveyed existing IDSs used for securing IoT-based smart environments such as Network Intrusion Detection Systems (NIDS) and Hybrid Intrusion Detection Systems (HIDS). They provided benefits and drawbacks of diverse anomaly-based intrusion detection methods, such as signal processing model, protocol model, payload model, rule-based model, machine learning, and others, where machine learning techniques require a brief overview without discussing the vulnerability of those ML-based systems to adversarial attacks. The study in [32] presented a thorough examination of ML-based attacks on IoT networks, offering a classification of these attacks based on the employed ML algorithm. The authors sought to explore a range of cyberattacks that integrated machine learning algorithms. However, adversarial attacks received only a brief discussion as one category of ML-based attacks, with mention of three adversarial attacks: the Jacobian-based Saliency Map Attack (JSMA), DeepFool, and the Carlini and Wagner (C&W) attack, as well as defense methods but they lack in-depth discussion. In [33], [PERSON] et al. surveyed adversarial threats that exist within the context of Cyber-Physical Systems (CPS). CPS is a subset of IoT, where the connection between cyberspace and physical space is provided by actuators and sensors. As a result, the work presented in [33] was limited to sensor-based threats only, which are a subset of network-based and side-channel attacks in the attack taxonomy of IoT networks. He et al. [34] explored the disparity in adversarial learning within the fields of Network Intrusion Detection Systems (NIDS) and Computer Vision. They accomplished this by reviewing the literature on adversarial attacks and defenses against IDS, with a special focus on IDS in traditional networks. The authors limited their study to evasion attacks only, considering that NIDS are typically created in secure environments, in which case the external attackers lack access to the training data set. Furthermore, the authors provided a taxonomy related to NIDS and not to adversarial attacks themselves. In light of the information presented above and summarized in Table 1, there is a notable scarcity of published surveys specifically addressing adversarial attacks against ML-based security systems in IoT networks. The limited number of existing surveys tend to have a narrow focus on the issue, with some solely concentrating on ML-based IDSs, while disregarding the wider scope, which encompasses ML-based MDSs and ML-based DISs. Also, some have been focusing primarily on insider threats while neglecting external ones. Additionally, certain surveys exclusively examine black-box attacks, overlooking white-box attacks. To bridge these gaps, this survey offers a comprehensive review of the current research landscape regarding adversarial attacks on IoT networks, with a special emphasis on exploring the vulnerabilities of ML-based IDSs, MDSs, and DISs. The survey also describes and classifies various adversarial attack generation methods and adversarial defense methods. To the best of our knowledge, this survey will be the first attempt of its kind to comprehensively discuss the holistic view of adversarial attacks against ML-based IDSs, MDSs, and DISs in the context of IoT, making a significant contribution to the field. This paper's contributions are outlined as follows:  
1. Revising and redefining the adversarial attack taxonomy for ML-based IDS, MDS, and DIS in the IoT context. 2. Proposing a novel two-dimensional-based classification of adversarial attack generation methods. 3. Proposing a novel two-dimensional-based classification of adversarial defense mechanisms. 4. Providing intriguing insights and technical specifics on state-of-the-art adversarial attack methods and defense mechanisms. 5. Conducting a holistic review of the recent literature on adversarial attacks within three prominent IoT security systems: IDSs, MDSs, and DISs.