Review of Adversarial Attack Mechanisms, Detection Techniques and Defence Strategies in Critical Network Infrastructures

Many of the critical networks are now vulnerable to complex security threats, especially those launched by adversaries against the machine learning-driven security systems used by these networks. Such attacks take advantage of weaknesses in AI systems by perturbing the model with carefully designed perturbations, which result in misclassification of malicious content as benign, compromising the system's confidentiality, integrity, and availability. The adversarial threat is unlike traditional cyberattacks; it is dynamic, adaptive and can circumvent traditional intrusion detection capabilities. This paper provides an extensive literature review on the adversarial attack methods, detection and defence techniques of critical network infrastructures. This review includes peer-reviewed publications published between 2019 and 2024 from the leading academic databases such as IEEE Xplore, SpringerLink, ScienceDirect and Google Scholar. The total number of studies analyzed were 48, covering contributions in the fields of creating adversarial attack methods, machine learning and deep learning based detection methods, and mitigation techniques. The results indicate that adversarial attacks can be divided into the following categories: evasion attacks, poisoning attacks, and exploratory attacks, where some of the more sophisticated methods, including those based on gradient, optimization, and reinforcement learning, are very effective in evading security systems. Current solutions, however, suffer from limited real-time adaptability, cross-domain generalization ability, explainability and integration across the attack lifecycle. While there are several defence mechanisms proposed, such as adversarial training, anomaly detection, and input transformation, existing defences have difficulties in being adaptable in real time, cross-domain generalizable, explainable and suitable for certain phases of the attack lifecycle. The study highlights a number of critical research challenges such as the lack of a common defence framework, inadequate real-time detection capabilities, absence of a standardized data sets and poor ability to withstand adaptive adversaries. The paper suggests the creation of multi-strategic, adaptive, and real-time adversarial threat management systems that can sustain themselves in a heterogeneous network environment.