The referenced study stresses the importance of research and development in this field, suggesting that deceptive virtual hosts can contribute significantly to the cybersecurity of industrial control networks. Probability-based models can assess the effectiveness and performance of cyberdeception techniques, which may help to make them more efficient and secure [4]. Probability models can be employed to predict the success rates of cyberdeception techniques, the probability of detection, and how attackers will react to these techniques; thus, cyberdeception strategies can be better optimized, and cyberdefense mechanisms can be strengthened. Combining mobile target defense and cyberdeception techniques effectively prevents cyberattacks on IoT systems and increases their security [5]. This approach will likely reduce attackers' success and enable cybersecurity teams to react to threats more quickly and effectively. Cyberdeception technologies, particularly honeypots and honeytokens, can be used in hybrid cyberdefense strategies. How can these technologies increase cybersecurity? Researchers argue that such technologies will help to strengthen cybersecurity by misleading cyberattackers, denying them access to natural systems and data, and providing valuable intelligence to understand attackers' real intentions and abilities [6]. A study [7] examines the possibility of stealthy cyberattacks toward an IDS involving function code attacks, injection attacks, and reconnaissance attacks, improving its robustness to adversarial attacks. The results show that the detector's robustness to adversarial samples increases after training on a mixture of the original dataset and newly produced samples. A timeline analysis of the effect of deceptive patches is presented, and, finally, a formal model of deceptive patches investigating the theoretical security of deceptive patches is analyzed. A framework employing the traditional software patching lifecycle is introduced,and the following steps are added to generate different versions of the released patches. The metrics that trigger the release of the diversified patches in question are discussed [8]. The study in [9] suggests GRN, an interpretable multivariate time series anomaly detection method based on neural graph networks and gated recurrent units (GRUs). GRN can automatically learn the possible correlations between sensors from multidimensional industrial control time series data. The experimental findings show that the model is more interpretable and provides more effective solutions. The proposed approach in [10] combines a structure learning approach with graph neural networks and utilizes attention weights to ensure explainability for the anomalies detected. The experiments conducted on two real-world sensor datasets with ground truth anomalies demonstrate that the method detects anomalies more accurately than baseline approaches. The current research aims to divert attention from fundamental system components and security vulnerabilities by giving false or misleading information to attackers, thus preventing them from using industrial control systems (ICSs) as a proactive defense mechanism against cybersecurity vulnerabilities in such systems. Furthermore, it proposes an artificial intelligence-based hybrid model to ensure cybersecurity in industrial control systems. The model is innovative in combining deceptive patch and window size manipulation techniques. The present study aims to integrate and examine deceptive patch solutions with window size manipulation and adversarial training methods to increase cybersecurity in industrial control systems. Adversarial training is a method developed to detect and prevent cyberattacks against artificial neural networks and is thought to strengthen cybersecurity in ICSs when combined with deceptive patch solutions. In general, the purpose of deception patches in protecting ICSs is to provide an additional layer of defense against cyberattacks by deceiving attackers and providing an early warning of a potential attack.