## 1 Introduction
Hence, the misclassification of ML initially appeared approximately two decades ago and has piqued researchers' interest. The researchers in [12] deceived spam classifiers into injecting some changes into an email. Moreover, it is even older than 38 years, according to the authors in [13], when they showed that false fingerprints might be made with plastic-like materials to deceive biometric identity recognition systems. Along with ML technology's significant advancement in network security, it exposes a new attack surface for attackers. Accordingly, the IDS is susceptible to adversarial attacks since it is built on ML, which could be compromised by crafting adversarial input against ML/DL models such as the artificial neural network (ANN), the deep neural network (DNN), and the support vector machine (SVM), affecting its accuracy and robustness. Furthermore, research has also demonstrated that adversarial samples could affect ML-based IDSs [10,14]. As a result, ML can also be fooled, necessitating some protection mechanisms. Additionally, the system becomes susceptible due to communication on the open network, which also gives enemies a massive attack surface [15]. In contrast, the adversarial sample inputs to ML deceive the model, causing the model to provide an incorrect result. Thus, IDSs based on ML may be harmed, affecting classification. Consequently, the ML classifier in cybersecurity is used to defend against and detect malicious attacks, but the big issue here is who will protect the defenders and how ML can withstand these attacks and provide correct categorization. Therefore, this challenge drives researchers to improve the resilience of ML algorithms. This paper presents an overview of ML methods and clarifies adversarial attacks on IDSs. Additionally, it provides a thorough literature review on the security and robustness of ML/DL models when applied to the development of IDSs. Above all, it is essential to emphasize that this study aims to provide a thorough overview of the impact of adversarial samples raised by using ML and DL in IDSs and to present potential solutions to these problems. To sum up, the particular contributions of this paper are as follows:  
* We analyze related surveys in the field of AML. * We present a general overview of the use of ML on an IDS in order to enhance its performance. * We clarify all types of adversarial attacks against ML and DL models and the differences between them, in addition to the challenges that face the launch of adversarial attacks. * We display the adversarial attacks launched against ML/DL-based IDS models in particular. * We present the different types of defense strategies to address adversarial attacks. * We investigate the gaps in the related literature and suggest some future research directions. The remainder of this paper is structured as follows: Section 2 discusses related existing surveys. Then, an overview of ML is presented in Section 3. Next, adversarial ML is introduced in detail in Section 4. Next, the studies that implemented the adversaries against IDSs are presented in Section 5. Then, the benchmark dataset is shown in Section 6. Next, the defense strategies against adversarial attacks in the two domains of computer vision and network security are presented in Section 7. After that, the challenges and future directions are given in Section 8. Finally, this paper is concluded in Section 9.