# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Artificial Neural Networks (ANN)  
In a study conducted by [13], an Artificial Neural Network (ANN) was used to evaluate the performance of the NSL-KDD dataset. With the proliferation of smart devices and technologies, monitoring abnormal internet traffic has become a crucial security concern. Various attacks pose significant threats to systems, causing them to slow down and impede their processing speeds. Intrusion detection systems play a vital role in assessing system security by raising alerts when an intrusion is detected. In this research, the authors utilized ANN to test the effectiveness of the NSL-KDD dataset. They evaluated both binary and five-class categorizations, which focused on different types of attacks. The results show that both categorizations yielded similar outcomes in terms of performance. Multiple performance indicators were employed to assess the results, and they demonstrated higher accuracy levels. Specifically, the NSL-KDD dataset achieved an 81.2% detection rate for intrusion detection and a 79.9% detection rate for malware detection. The proposed system outperformed existing algorithms in terms of detection rate for both binary and five-class classification problems. These findings highlight the effectiveness of ANN in enhancing the detection capabilities of intrusion detection systems using the NSL-KDD dataset. In their study published in 2013, Ref. [14] introduced a novel approach called \"Flow-Based Anomaly Detection Using a Neural Network Optimized with the GSA Algorithm\". With the increasing demand for reliable high-speed networks to support the growing number of Internet of Things applications, a network access detection system plays a crucial role in preventing network infiltration (NIDS). Traditional packet-based NIDS systems require significant time to analyze all network packets, making them unsuitable for processing large amounts of data in real-time. Flaw-based intrusion detection, which focuses on packet headers, is more suitable for high-speed networks. However, detecting intrusions has become increasingly challenging as the likelihood of further attacks continues to rise. Anomaly-based intrusion detection is a well-known approach to identify unknown threats. The researchers presented a flaw-based anomaly detection method in their study, utilizing an Artificial Neural Network (ANN) as a valuable tool for detecting abnormalities. Specifically, they employed a Multi-Layer Perceptron (MLP) with one hidden layer as the neural network architecture. To optimize the link weights of the MLP network, they applied a Gravitational Search Algorithm (GSA). The GSA-based flow anomaly detection algorithm (GFADS) was trained using flaw-baseddata. The results showed that the trained system achieved an impressive accuracy of 99.43% in distinguishing between benign and harmful signals. The performance of the GSA algorithm was compared with other optimization techniques such as Particle Swarm Optimization (PSO) and gradient descent. According to the findings, GFADS demonstrated the ability to identify flaw-based irregularities effectively, making it a valuable tool for network intrusion detection. The study highlighted the strength of the proposed approach, optimized with the GSA algorithm, as it offered a comprehensive set of traits for detecting network anomalies. In their study conducted in 2018, Ref. [15] explored Anomaly Detection in Hybrid Electric Vehicles using an ANN-based Support Vector Data Description. One of the challenges faced by machine learning classifiers is the \"dimensionality plague\", where finding data from the abnormal class becomes extremely difficult. One-class classifiers address this issue by splitting the input into groups using training examples from a large library of common events. However, since there are no training data available for the anomalous class, the accuracy of these classifiers suffers. To overcome this limitation, the researchers employed a two-class, ANN-based classifier that utilized the class discoveries of a single-class classifier.