# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 3 Problem Statement  
The goal of this study is to develop a methodology and create a framework for predicting banking malware using machine learning approaches. Many malware detection approaches already exist and have been researched and used by researchers. Some of these include signature-based approaches [51, 52] and anomaly-based detection approaches [53, 54], however, these do have limitations [55]. Some of these limitations include the following.  
* Signature-based systems are unable to detect zero-day malware or unknown malware variants.
* Signature-based systems must be updated frequently to accommodate newly emerging malware variants.
* Malware uses various obfuscation techniques to evade detection.
* There can be a time delay between discovering new malware and creating a signature to identify the malware.
* Signature databases can consume significant system resources and have a slow performance.
* Modern malware can dynamically change its structure (polymorphic malware) or rewrite its code (metamorphic malware) to avoid signature-based malware systems.
* As the malware landscape evolves, maintaining and updating the signature database becomes increasingly complex.
* Effective and continuous tuning is required to reduce false positives.
* The network has to be baselined, and normal communication traffic needs to be identified.
* Network traffic must be constantly monitored.
* Malware can hide within the normal traffic flows, making these malware types difficult to detect.  
Machine learning has been used to resolve these issues, and while researchers have used machine learning algorithms to detect banking malware [35, 44, 45, 46, 47], there has been minimal research aimed at detecting a wide range of banking malware variants using a model trained exclusively on one dataset containing a single banking malware variant. This study seeks to address this gap by developing a machine learning model trained on a single dataset representing one variant of banking malware. The primary objectives of this research are as follows:  
* Cross-Variant Detection: To apply the trained model to identify other banking malware variants and evaluate its generalizability.
* Algorithm Performance Evaluation: To compare the detection performances of various machine learning algorithms in this context.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Test Log-Loss (\%)** & **Misclassification Rate** & **Accuracy** \\ \hline KNN & 0.24 & 4.5 & 95.5 \\ \hline Logistic regression & 0.528 & 12.32 & 77.68 \\ \hline Random forest & 0.085 & 2.02 & 97.98 \\ \hline XGBoost & 0.078 & 1.24 & 98.76 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Experimental results of the algorithms when classified using byte file features.  
* Feature Optimization: To determine the minimum set of features required to achieve satisfactory prediction results, thereby optimizing computational efficiency and simplifying the detection process. This approach aims to advance the understanding of cross-variant malware detection and provide insights into the effectiveness of machine learning algorithms and feature selection when used to detect diverse banking malware threats.