# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 3 Background and Related Work
### Applications and Challenges of Machine Learning in Cybersecurity
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Survey** & **Objective** \\ \hline \multirow{3}{*}{[8]} & & \multicolumn{2}{c}{Analyzes applications of ML for prediction and} \\  & & \multicolumn{2}{c}{classification in cybersecurity, emphasizing} \\  & & \multicolumn{2}{c}{improvement in error reduction and advanced} \\  & & \multicolumn{2}{c}{threat detection.} \\ \hline \multirow{3}{*}{[9]} & \multicolumn{2}{c}{Machine learning in cybersecurity: A review of threat detection and defense mechanisms} & \multicolumn{2}{c}{Examines the role of ML in automating threat detection and defense strategies, exploring supervised} \\  & & \multicolumn{2}{c}{and unsupervised methods.} \\ \hline \multirow{3}{*}{[10]} & \multicolumn{2}{c}{A systematic literature review on machine learning in} & \multicolumn{2}{c}{Summarizes 73 papers on ML techniques in object detection for cybersecurity, offering data visualizations and trends.} \\ \hline \multirow{3}{*}{[27]} & \multicolumn{2}{c}{A systematic review of defensive and offensive cybersecurity with machine learning} & \multicolumn{2}{c}{Reviews ML techniques for offensive and defensive cybersecurity, highlighting frequently used methods, datasets, and promising approaches.} \\ \hline \multirow{3}{*}{[28]} & \multicolumn{2}{c}{Network and cybersecurity applications of defense in adversarial attacks} & \multicolumn{2}{c}{Discusses ML-based defense mechanisms for adversarial attacks, categorizing techniques into enhancement, mitigation, and innovative solutions.} \\ \hline \multirow{3}{*}{[29]} & \multicolumn{2}{c}{Machine learning in cybersecurity: Applications, challenges, and future directions} & \multicolumn{2}{c}{Explores ML techniques for intrusion detection and malware classification, highlighting emerging threats like adversarial attacks and proposing resilient architectures.} \\ \hline \multirow{3}{*}{[30]} & \multicolumn{2}{c}{Functionality-preserving adversarial machine learning for robust classification} & \multicolumn{2}{c}{Reviews adversarial ML techniques in cybersecurity, focusing on functionality-preserving attacks and robust classification methods.} \\ \hline \multirow{3}{*}{[31]} & \multicolumn{2}{c}{Machine learning in cybersecurity: A review} & \multicolumn{2}{c}{Discusses the use of ML for malware analysis, intrusion detection, and anomaly detection in critical infrastructure.} \\ \hline \multirow{3}{*}{[21]} & \multicolumn{2}{c}{Machine learning and deep learning methods for cybersecurity} & \multicolumn{2}{c}{Surveys ML/DL methods and datasets for intrusion detection and anomaly detection, emphasizing the challenges in implementation.} \\ \hline \multirow{3}{*}{[23]} & \multicolumn{2}{c}{Cybersecurity threat detection using machine learning} & \multicolumn{2}{c}{Evaluates ML techniques for malware, phishing, and network intrusion detection, addressing algorithmic bias and adaptation challenges.} \\ \hline \multirow{3}{*}{[22]} & \multicolumn{2}{c}{A systematic review of AI and ML techniques} & \multicolumn{2}{c}{Reviews classification algorithms like SVM and decision trees in cybersecurity, offering a taxonomy of methods.} \\ \cline{3-5}  & \multicolumn{2}{c}{} & \multicolumn{2}{c}{Explores ML applications for detecting malware, security breaches, and anomalous behaviors in software systems.} \\ \hline \multirow{3}{*}{[32]} & \multicolumn{2}{c}{Data mining and machine learning in cybersecurity} & \multicolumn{2}{c}{Provides a comprehensive guide to ML and data mining solutions for cybersecurity problems like intrusion detection and anomaly detection.} \\ \cline{3-5}  & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \end{tabular}
\end{table} 
Table 1: Applications and challenges of machine learning in cybersecurity.