# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Random Forest  
Ref. [20] employed a random forest algorithm to detect anomalies. With the increasing sophistication of hackers, having a deterrent device like an Intrusion Detection System (IDS) has become crucial for data security management. The purpose of an intrusion detection system is to identify and prevent potentially harmful network activity. An effective intrusion detection system consists of a well-trained classification model capable of detecting attackers. In their study, the researchers evaluated the performance of IDS using a random forest classifier and two metrics: accuracy and false alarm rate. They conducted experiments using three public intrusion datasets: NSL-KDD, UNSW-NB15, and GPRS. To find the best learning parameters, a grid search was conducted with various tree-size ensembles. The results showed that the random forest model outperformed other approaches such as the matching ensemble (random tree + naive Bayes tree), as well as standalone classifiers like naive Bayes and neural networks, in terms of k-fold cross-validation for IDS. Ref. [21] delved into the topic of fault detection using random forest similarity distance. With the constant drive to keep up with Moore's law and achieve higher device performance, semiconductor manufacturers have been continuously reducing and reconfiguring transistor layouts. This has led to increasingly complex manufacturing processes, with hundreds of processing steps involved in most cases. In such a complex manufacturing environment, even a single improperly processed wafer can have a significant negative impact on profitability if it progresses through the entire production process. To address this issue, the authors propose an unsupervised technique that utilizes chemical fingerprints obtained during the plasma etching process to identify damaged wafers. They employ random forests as the basis for their approach. The effectiveness of the proposed strategy is evaluated using both a simulated case and a real-world industrial dataset. The results demonstrate that the technique successfully identified defective wafers in both scenarios, indicating its potential utility in practical applications. Ref. [22] conducted a study on the application of random forests for identifying network-based anomalies on active routers. Intrusion detection systems (NIDSs) play a crucial role in network security infrastructure. Rule sets are commonly used to govern the functionality of NIDSs. However, defining these rules can be a challenging and time-consuming task due to the vast amount of network data. To overcome this challenge, data mining techniques are employed to create adaptive intrusion detection models. Random forests, a powerful data mining tool, are utilized for detecting network intrusions in real-time. The research introduces methods for feature selection and optimizing random forest parameters. Multiple models are compared, and an alternative method for detecting anomalies in active networks is proposed. Overall, the study highlights the potential of random forests as an effective approach for network intrusion detection and presents techniques to enhance their performance in detecting anomalies across active networks. Ref. [23] conducted a study on network anomaly detection using Random Forests and Traffic Entropy. Detecting network anomalies requires monitoring fluctuations in traffic feature distributions and utilizing them to identify traffic with varying behavior. One effective approach is to use Shannon entropy to identify anomalies in network data by detecting deviations from the expected patterns. Standardized entropy provides a measure of homogeneity and randomness, allowing for comparison across multiple sample spaces and variables. Random Forests, a machine learning classification technique, is well-suited for handling challenging or imbalanced data structures commonly encountered in small to moderate-sized datasets.