# Preventing the Cloud Networks through Semi-Supervised Clustering from Both Sides Attacks
## 5 Comparative Analysis  
Many researchers have designed different algorithms to secure the cloud servers and have developed many tools to reduce the attacking ratio from the cloud servers. Some researchers have surveyed most of the papers and tried to implement the best techniques to secure the cloud servers, but as the number of users in the cloud has increased, the attack ratio has also increased. If the cloud administrator has designed an encryption algorithm to secure the cloud data, the attackers have created a more efficient decryption algorithm to decrypt that data. Based on these, the attack ratio increased rather than decreased. A comparative analysis of the latest is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**S**\# & **1** & **2** & **3** & **4** & **5** & **Proposed Work** \\ \hline
**Reference No.** & [26] & [28] & [30] & [31] & [32] & \\
**Year** & 2016 & 2020 & 2020 & 2021 & 2021 & \\
**Outside Security** & IDS Server, & & \(X\) & SIDS, AIDS & \(X\) & HIDS, NIDS & SIDS, Network \\
**Techniques** & vmbr1, vmbr2 & & Used _TCP dump_ & & & & \\
**Inside Security** & IDS Server, & packet analyzer & & & & & \\
**Techniques** & vmbr1, vmbr2 & tool to extract & \(X\) & Anomaly Based & Cloudflare, Cloud & Access Control & System, Cloud \\  & & features from & & & IDS & Shell & Shell \\  & & packets & & & & & \\  & & Support Vector & & & C-K means & & & \\
**Algorithm** & Worked on a tool & Machine, Naive & & & & & \\  & named Snort & & Bayes, & & & & & \\  & & Unsupervised & & & & & \\  & & Learning & & & & & & \\  & & Unsupervised & & & & & & \\  & IDS can detect & & & & & & \\  & attacks from both & & & & & & & \\  & sides at a time & & & & & & & \\  & & & & & & & & \\  & & other algorithms & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of techniques.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fold Iterate** & **TP** & **FP** & **TN** & **FN** & **Se** & **Pr** & **Acc** & **Sp** & **E.R** & **F1** \\ \hline
1 & 70 & 3 & 25 & 2 & 97 & 95 & 95 & 89 & 5 & 95 \\
2 & 65 & 1 & 31 & 3 & 97 & 98 & 96 & 96 & 4 & 97 \\
3 & 61 & 7 & 27 & 5 & 92 & 89 & 88 & 79 & 12 & 90 \\
4 & 63 & 8 & 23  
In 2016 [26], researchers placed the Intrusion Detection System inside and outside the cloud server to protect the cloud server from attacks from both sides. They used Virtual Machine Based Rootkit-1 and Virtual Machine Based Rootkit-2 to connect the cloud server to different devices and worked on a tool to implement an Intrusion Detection System. They confirmed that IDS could be used to prevent cloud server attacks and concluded that, when an Intrusion Detection System is activated on the server, it will be only 2.5%. In 2019 [27], researchers worked on three algorithms to protect the cloud server from botnet attacks, used the TCP dump packet analyzer to extract features from botnet packets, and obtained different results in unsupervised learning 94.78%, support vector 84.32%, while Naive Bayes is 71.63%. A comparative analysis between previous work results with the latest work results is shown in Table 5.  
In 2020 [28], authors surveyed various papers, collected all the data sets used in these papers, and discussed whether, if all these techniques are implemented in the intrusion detection system, the cloud server can be prevented from attacks. In 2021 [29], the authors used outlier detection and semi-supervised clustering algorithms to secure cloud servers and used some real data sets on these algorithms. After that, lymphography and glass were used to perform simulations, and the algorithms' novelty was observed. However, no paper has experimentally proven how a cloud server can be protected from an attack if a cloud server is attacked, nor has any paper proved how this mechanism be avoided if an attacker on a cloud server uses a mechanism,.