# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Advanced Malware Detection Techniques
#### 3.6.13 Evaluation of Transfer Learning Techniques for Malware Detection
The data reveal a predominant use of models such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest (RF), underscoring their effectiveness in handling complex data representations. This visualization provides a clear overview of methodological preferences, facilitating the identification of prevailingt trends in algorithm selection, particularly in challenging environments such as Internet of Things (IoT) systems, where scalability, accuracy, and resilience are critical factors. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Title** & **Technique Used** & **Machine Learning** & **Benefits** \\ \hline Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification [67] & EfficientNetB1 with image representation & Yes & High precision (99\%) in malware classification, computational efficiency \\ \hline Mitigating adversarial evasion attacks of ransomware using ensemble learning [68] & Ensemble learning (Naïve Bayes, Decision Trees, Random Forests, SVM, Logistic Regression, AdaBoost) & Yes & High precision in ransomware detection, resistance to adversarial evasion attacks \\ \hline Effectiveness Analysis of Transfer Learning for the Concept Drift Problem in Malware Detection [70] & Transfer learning (TrAda, CORAL, DAE) & Yes & Improved precision in the presence of concept drift, efficient use of old data \\ \hline Fileless malware threats: Recent advances, analysis approach through memory forensics and research challenges [65] & Memory forensics & No & Detection and analysis of fileless malware, identification of malicious processes in memory \\ \hline Evaluation of Machine Learning Algorithms for Malware Detection [66] & Naive Bayes, SVM, J48, Random Forest, Decision Tree, CNN & Yes & High precision (DT 99\%), low false positive rate \\ \hline Phishing URLs detection using sequential and parallel ml techniques: Comparative analysis [76] & Random Forest, Naive Bayes, CNN, LSTM & Yes & High precision in phishing URL detection (CNN-LSTM 99.1\%) \\ \hline Distributed deep neural network-based middleware for cyber-attacks detection in smart IoT ecosystem: A novel framework and performance evaluation approach [77] & Distributed deep neural network (DNN) & Yes & High precision in IoT cyber-attack detection \\ \hline An ameliorated multiattack network anomaly detection in distributed big data system-based enhanced stacking multiple binary classifiers [52] & Binary classifier stacking ensemble & Yes & High precision in network anomaly detection \\ \hline Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey [47] & Adversarial machine learning (AML) & Yes & Improved robustness of classification, functionality preservation \\ \hline Phishing URLs detection using machine learning techniques [78] & K-Nearest Neighbors, Decision Trees, SVM, Random Forest & Yes & High precision in phishing URL detection (RF 97.6\%) \\ \hline Detection of phishing attacks using sequential and parallel machine learning techniques [71] & Random Forest, Naive Bayes, CNN, LSTM & Yes & High precision in phishing URL detection (CNN-LSTM 99.1\%) \\ \hline Deep learning approaches for malware detection [72] & LSTM, CNN & Yes & High precision in malware detection (LSTM 98.5\%) \\ \hline Anomaly detection in networks using machine learning [73] & Random Forest, SVM, Naive Bayes & Yes & High precision in network anomaly detection (RF 95.4\%) \\ \hline Detection of cyber-attacks in IoT environments using machine learning techniques [74] & Decision Tree, Random Forest, SVM techniques [74] & Yes & High precision in IoT cyber-attack detection (RF 96.2\%) \\ \hline Detection of ransomware using machine learning techniques [75] & K-Nearest Neighbors, Decision Tree, Random Forest & Yes & High precision in ransomware detection (RF 97.8\%) \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of Malware Detection Techniques and Studies.