# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
## 2 Related Work
Most importantly, our method is specifically tailored for P2P botnet detection and is rigorously evaluated using the real-world CTU-13 Scenario 12 dataset, thereby offering a novel, scalable, and practical solution that addresses the limitations found in the existing literature. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Sl. No.** & **Author** & **Botnet Type** & **Dataset Used** & **Category of FS** & **FS Algorithm** & **Classification** & **Performance** & **Disadvantage** & **Advantage** \\  & **Detected** & **Detected** & & **Algorithm** & **Used** & **Algorithm** & **Matrix** & & \\ \hline
1 & [13] & IRC & Testbed & Swarm-and evolution-based MH & AFSA, GA & SVM & Acc—100\% & IRC botnet is used. Only accuracy is calculated for performance evaluation. & Metaheuristic algorithm is used for FS. \\ \hline
2 & [14] & IRC & Testbed & Swarm-and evolution-based MH & PSO, GA & BPN & Acc—95.09\% & IRC botnet is used. Only accuracy is calculated for performance evaluation. & Metaheuristic algorithm is used for FS. \\ \hline
3 & [15] & P2P & ISOT & Swarm intelligence-based MH & PSO & Majority voting & Acc—99.64\%, Re–99.82\% & Precision and FI are not calculated. & Metaheuristic algorithm is used for FS. \\ \hline
4 & [16] & P2P and IRC & ISOT and BCX & Evolution-based MH & GA & Decision tree (C4.5) & Re–99.46\% & Accuracy, precision, and FI are not calculated. & Metaheuristic algorithm is used for FS. \\ \hline
5 & [8] & Not mentioned & UNSW—NB15 and CICID52017 & Evolution-based MH & GA & Decision tree & Pre—99.17\%, Re–96.85\% & Botnet type detected is not mentioned. & Metaheuristic algorithm is used for FS. \\ \hline
6 & [17] & Not mentioned & N-BaloT & Human-behavior-based MH & WCC & SVM & Acc—95\%, Re–95\%, Re–95\% & IRC botnet is detected. & Metaheuristic algorithm is used for FS. \\ \hline
7 & [18] & Not mentioned & CICIDS017 & Filter method & PCA, LDA & NB, SVM & Acc—99.68\% & Detection performed for centralized or not known. & High accuracy. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comprehensive summary of existing work on ML-based botnet detection with their advantages and disadvantages. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sl. No.** & **Author** & **Botnet Type** & **Dataset Used** & **Category of FS** & **FS Algorithm** & **Classification** & **Performance** & **Disadvantage** & **Advantage** \\  & **Detected** & & **Algorithm** & **Used** & **Algorithm** & **Algorithm** & **Matrix** & & \\ \hline
8 & [19] & P2P & CTU-13 & Filter method & PCA, Univariate & SVM, logistic & Acc—99\%, & Lots of FS algorithms & High accuracy. P2P \\  & & & & Selection, RFE, Feature & regression, KNN, & AUC—99\%, & are compared but all & detection is \\  & & & & Importance, & decision tree & AR—99\% & methods. & \\  & & & Correlation Heat & & & & & \\ \hline
9 & [20] & P2P & ISOT, MCP & Filter method & Information Gain & SVM, Naive Bayes, & Not mentioned & Information about & FS is conducted and three different \\  & & & and NMS & & & & & \\  & & & & & Bayesian Network, & not given. & datasets are used. \\  & & & & & Ada Boost, Random & & & \\  & & & & Forest, JB, PART & & & & \\ \hline
10 & [21] & Not & Cyber Clean & Filter method & PCA and & Logistic Regression, & Acc—97\% & Detection conducted for & High accuracy. \\  & mentioned & Center (CCC) & & Information Gain & SVM, Random Forest & TPR—97\% & centralized or & \\  & & & dataset & & and Multilayer & FPR—0.7\% & decentralized botnet is & \\ \hline
11 & [22] & P2P & MCPF & Filter method & Recursive Feature & ANN & Acc—99.04\% & Lots of FS algorithms & High accuracy. P2P \\  & & & & Selection, PCA, & Pre—99.35\% & are compared but all & detection is \\  & & & & Correlation & Re—99.35\% & belong to the same & conducted. \\  & & & Matrix, Univariate & & F1—99.21\% & category. & \\  & & & Selection, Feature & Importance & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cost.