## 5 Comparative Analysis  
Researchers worked on different algorithms and techniques to protect data from replay attacks. Some researchers have used protocol-based security to prevent data from being attacked. Some researchers have undertaken detection by implementing machine-learning models on different datasets, and some researchers surveyed various papers and collected efficient techniques that can be used for detecting replay attacks, but these techniques are not enough to protect data from replay attacks. If an attacker makes replay attacks, the attacker can be detected, and the attacker's IP address can be blocked, but the data cannot be secured. In the paper [22], the researcher identified the problems of various replay attacks to show the reasons for replay attacks and collected various techniques to solve these problems, and discussed the effective techniques among these techniques. Instead of discussing these techniques if these techniques are implemented in architecture and discussing the new features of these techniques, such techniques can be used to protect data from replay attacks, but better techniques are collected in this paper and are discussed, which is the drawback of this paper. In paper [23], a deep-learning-based model was developed to detect replay attacks. This algorithm was implemented on an application, and the performance of the application was identified by applying different datasets to this application, and it was then discussed that normal and abnormal packets could be identified with the help of this deep learning algorithm. Normal and abnormal packets can only be distinguished with the help of this algorithm. If, instead of distinguishing between normal and abnormal packets, the data is protected from the attacker, the cloud data can also be protected, which is the drawback of this paper. In paper [24], to detect the replay attacks in the control system network, a Kullback-Leibler divergence algorithm was used in which the packet-dropout feature was used, and the replay attacks were detected. If the data is stored in an encrypted format instead of replay attacks in the control system network, then a replay attack cannot happen on this data. If a replay attack occurs, the value obtained from the replay attack and actual value can be easily determined, which is not undertaken in this paper and is the drawback of this paper. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Sr \#**} & \multicolumn{1}{c}{**User Name (U)**} & \multicolumn{1}{c}{**Plain Text (P)**} & \multicolumn{1}{c}{**Key (K)**} & \multicolumn{1}{c}{**Cipher Text**} \\ \hline \multirow{3}{*}{1} & W@jiba & Hello & Cloud &
\begin{tabular}{c} x2\(  
In paper [25], a control area network was designed using three nodes to detect replay attacks. The data was broadcast in two categories to detect replay attacks: the first was to broadcast the complete message, and the second was to broadcast the partial message. After broadcasting, it was discussed that it is challenging to detect replay attacks when the partial message is broadcasted in the network. Whenever a replay attack occurs in a cloud server, it can be performed using any mechanism. If the data at the end of the cloud server is encrypted with different keys, the name of the sender and receiver, then any replay attacks on the data can be easily detected, which is the drawback of this paper. In paper [26], researchers developed a hybrid deep learning algorithm to detect DDOS and replay attacks. The algorithm was implemented on three different datasets, and the algorithm's performance was determined by performing DDOS and replay attacks. It was then discussed that DDoS could be easily detected based on its behavior, but replay attacks cannot be easily detected. If such a mechanism is used in the cloud server to help detect such attacks, as soon as there is a replay attack or a change in the data is broadcast, in this paper, no such techniques are discussed. That is the drawback of this paper.