# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Support Vector Machine (SVM)  
Ref. [6] used Support Vector Machines and Automatic Feature Selection to hunt for anomalies in Earth Dam and Levee Passive Seismic Data. They aimed to monitor the condition of earth dams and levees and automatically identify unexpected events in passive seismic data by developing a unique data-driven method to various time-series data-driven systems. Applying the machine learning technique to the geophysical data collected from surface sensors, [PERSON] et al. detected internal issues within the levee structure. Ref. [6] used two distinct datasets from experimental laboratory earth embankments comprising approximately 80% normal instances and 20% anomalies. Their study employed wavelet-based algorithms to decompose time series data segments and extract nine spectral components, resulting in a two-class SVM with an overall accuracy of over 94% and an F1-score of 96% when using 10-fold cross-validation. Experiments using the optimum settings increased the overall accuracy from 83% to over 91% and a 95% F1 score. The findings imply that humans can distinguish between normal and abnormal data. Ref. [7] conducted research on anomaly detection in EtherCAT-based water level control automation where there was a significant threat of cyber-attacks in Industrial Control Systems (ICS). EtherCAT is a European real-time protocol prone to both known and unknown assaults targeting critical field systems. To alleviate this problem, [PERSON] et al. developed a laboratory-scale water level control test, generating attack vectors based on EtherCAT communication principles and creating a dataset. After minimizing operational process behavior, they explored both supervised and unsupervised approaches for anomaly detection. Their study demonstrated effective supervised algorithms such as Support Vector Machines (SVM) and Random Forest in detecting EtherCAT abnormalities, while the unsupervised methods proved successful in identifying specific attack patterns. Thus, they demonstrated that machine learning techniques contributed to enhancing the security of EtherCAT networks and strengthening Industrial Control Systems against cyber threats. Ref. [8] suggested a machine learning technique based on SVM and GA for detecting network anomalies. The researcher focused on machine learning approaches for detecting cyber-assaults based on Internet anomalies. The support vector machine (SVM) was employed for packet classification in the machine learning framework while the Genetic Algorithm (GA) was utilized for feature selection Experiments revealed that the proposed system outperformed current network intrusion detection system (NIDS). Their main objective of the research was to develop an Enhanced SVM technique that offered a standardized framework for identifying and categorizing new network attacks. They demonstrated faster data processing and higher accuracy in field selection compared to other techniques. The Enhanced SVM combined a high-performance supervised method with a non-supervised, unlabeled approach. Their SVM technique predicted a low false-positive rate similar to NIDS, but without the need for labeled data required by signature-based NID methods. Ref. [9] developed an anomaly detection framework for medical wireless sensor networks, utilizing SVM and linear regression models. Their research focuses on widespread patient and healthcare monitoring, employing data mining, machine learning, and sensor fusion technologies. Their framework distinguishes between irregular variations in patient physiological parameters and faulty sensor data, ensuring reliable operations and global real-time monitoring from smart devices. The program utilizes a Support Vector Machine to classify anomalous sensor data, enabling the identification of critical stages or erroneous measurements.