## 2 Literature Review
IoT technology primarily serves as a platform for service automation by allowing various things to communicate over the Internet backbone. On the other hand, smart public infrastructure is prone to cyber due to the security vulnerabilities connected with IoT networks. This paper created a hybrid deep learning model for identifying replay and DDoS attacks in a real-world smart city platform. DDOS attacks attempt to access data without permission, while replay attacks attempt to insert malicious data. Both types of attacks can cause damage to the cloud server. To check the performance of the Hybrid Model, three real-time, smart-city datasets (smart river dataset, smart soil dataset, environmental dataset) were taken, and DDOS and replay attacks were performed on these datasets. It was then discussed whenever DDoS and replay attacks are performed on a cloud server. DDoS attacks can be easily detected on the behavior of DDoS, but when the same message is received from two users, it is difficult to detect the original and malicious message in such messages. According to [PERSON] et al. [27], the supervisory control system and data acquisition system (SCADA) is the core component of the Industrial Control System (ICS), with the help of which the industrial control system is being controlled. This component operates within its network and implements a proprietary protocol, making it less vulnerable to attacks on the cloud network. According to researchers, industrial control systems are used for data sharing and remote access on the Internet and local networks, which increases the likelihood of network attacks. Cyber-attacks on ICS are far more likely now than before, and these attacks can be both internal and external. This paper used the Modbus protocol to prevent Command Injection Attacks, Replay Attacks, and False Access Injections. First, the SCADA testbed technique had been set, and the effects of these attacks on the testbed data had been shown, for which a local area network was taken and simulated by carrying out various attacks on this network. After launching various attacks on the LAN, an algorithm Modbus protocol was developed to prevent replay attacks, false-access injection, and command-injection attacks. After that, the attack block rate (ABR) was used to check the algorithm's performance, which blocked 97% of the attacks and saved the SCADA system from being attacked. Different researchers have used different techniques and algorithms to protect cloud data from replay attacks. Some researchers have tried to identify the causes of replay attacks [22], which have identified all the problems that cause replay attacks. After that, various solutions have been provided that can be used to protect against replay attacks. Some researchers [23] developed deep-learning algorithms that can be applied to an application and determine the application's characteristics by different datasets. Some researchers [24] developed an algorithm to protect the control system network from replay attacks. Some researchers [25] designed a three-node control area network to detect replay attacks on partial broadcast messages, arguing that such replay messages are difficult to detect, while other researchers [26] designed a hybrid deep learning model that had capabilities to detect DDoS and replay attacks. Various datasets have been implemented on this model and it has been discussed that DDoS attacks are easier to detect than replay attacks based on their behavior. No algorithm has been developed in any paper to detect replay attacks and protect data from replay attacks. No such technique has been used in any paper in which the information of each user is stored along with the user's data. Cloud data can be protected from replay attacks if cloud cryptography is used to protect against replay attacks. If an attacker performs a replay attack, based on the data transmitted from the replay attack, it can be identified whether the incoming data is malicious or authentic, and whether the data has been altered or not.