## 1 Introduction  
Machine learning (ML) approaches are changing our perceptions of the world and affecting every aspect of our lives in the age of technology, such as autopilot, facial recognition, and spam detection. A distinctive feature of ML is that instead of designing the solution with coding, the programmer creates a method to discover the key to a problem using samples of other issues that have been solved. ML techniques can produce satisfactory results in many situations since machine-generated features are typically more reliable and representative than hand-crafted features [1]. Furthermore, ML procedures' training and evaluation phases are generally constructed assuming they are executed in a secure environment [1]. Therefore, the use of ML has expanded drastically, especially in cybersecurity, depending on providing a secure environment for users and institutions. Furthermore, due to the efficiency of ML in automatically extracting useful information from massive databases, the use of ML in cybersecurity meets the development of cyberattacks [2]. Various network attacks affect the users' or institutions' network systems, such as denial of service (DoS), distributed denial of service (DDoS), and SQL injection. Thus, cybersecurity specialists propose and utilize various types of defensive methods against network attacks, such as firewalls, intrusion detection systems (IDS), and intrusion prevention systems (IPSs). Such defense methods are used to detect or deter unauthorized network attacks that may affect network users in a harmful way. Furthermore, to serve cybersecurity specialists and beneficiaries, researchers proposed building their defenses based on ML techniques to improve the defense methods since a variety of network security techniques are increasingly using ML approaches for enhancements [1], for example, an IDS. Figure 1 shows some network security applications that can be improved with ML to protect the network against cyberattacks. IDS is one of the cybersecurity domains where machine learning is suitable. It is a type of computer security software that seeks to identify a wide range of security breaches, from attempted intrusions by outsiders to system penetrations by insiders [2]. In addition, the IDS evaluates the information from many sources and produces alerts when specific criteria are met [3]. In order to improve the IDS and make it more reliable against network attacks, the cybersecurity specialists suggested building it with ML, which achieves an effective result in classification and assists in resolving malware detection issues. ML-based IDSs can identify system anomalies with high precision, according to [4]. Consequently, ML-based IDSs yield several benefits, including increased accuracy and the detection of new attacks [5]. Furthermore, according to [6], an ML-based IDS produces superior results, recommending a filtering approach based on a support vector machine (SVM) classifier and the NSL-KDD intrusion detection dataset to detect suspicious network intrusion. ML systems are increasingly trusted in cyber-physical systems [7], including factories, power plants, and the oil and gas industries. In such complex physical surroundings, a threat that manages to get through a weak system could be harmful [8]. Despite the dependence on and faith in ML systems, attackers who want to avoid ML-based system discovery processes may use the inherent nature of ML, learning to recognize patterns, as a possible attack component [9]. As a result, attackers craft malicious inputs called \"adversarial samples.\" Adversarial samples are constructed by intentionally adding minor perturbations to initial inputs, which results in the misclassification of the ML/DL  
Figure 1: Network security applications against cyber attacks. models [10]. Adversarial machine learning (AML) based on the National Institute of Standards and Technology (NIST) is divided into four attacks, which are: evasion, extraction, poisoning, and inference [11].