# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Genetic Algorithm (GA)  
Ref. [28] devised a Network Anomaly Detection System by integrating Genetic Algorithms with Fuzzy Logic. With the proliferation of computer network usage across various applications, ensuring the integrity and availability of computer networks has become crucial for both individuals and businesses. To address this, the researchers utilized Flow Analysis, a technique that examines network flow data to estimate network traffic behavior over a specific period. They employed the Genetic Algorithm to generate a Digital Signature of a Network Segment based on this analysis. In contrast to existing approaches, the proposed system incorporated Fuzzy Logic to determine the abnormality of network occurrences. An expert system was designed to utilize IP flows, monitor network traffic, and establish expected behavior at regular intervals. Whenever a potential issue arises, users are promptly alerted. The suggested anomaly detection system automatically detects network faults and boasts impressive performance. In real network traffic flows, the approach achieved an accuracy of 96.53% and a false-positive rate of 0.56%. Comparative analysis with other approaches demonstrated its superiority. Ref. [29] the Tsfresh Tool and a Genetic-Algorithm-based Anomaly Detection Algorithm Selection Service for IoT Stream Data. While Anomaly Detection Algorithms (ADA) are readily available as services in maintenance management systems, the challenge lies in effectively dealing with the continuously changing stream data and the occurrence of idea drift in dynamic IoT environments. Selecting an appropriate Anomaly Detection Service (ADS) in real-time becomes a daunting task. To address this, the researchers developed a method for selecting and configuring ADS for real-time data anomaly detection. They employed the Tsfresh time-series feature extractor and a feature selection strategy based on a genetic algorithm to efficiently extract dominant features that represent stream data patterns. Historical data, including stream data features and other relevant information, were collected to dynamically identify important ADS at runtime. A fast classification model based on XGBoost was constructed to aid in the selection of suitable services and their configurations based on stream data patterns. The key to their approach's success lies in the features used to characterize and portray the underlying qualities of time-series data. Extensive tests were conducted to evaluate the effectiveness of the features obtained through the genetic algorithm. Experimental results on both synthetic and real datasets demonstrated that their proposed strategy outperformed previous complex approaches in terms of accuracy. It showcased the ability to identify suitable services across various scenarios. Ref. [29] proposed a novel approach, utilizing the Tsfresh Tool and a Genetic-Algorithm-based Anomaly Detection Algorithm Selection Service, for effective anomaly detection in IoT Stream Data. Nowadays, Anomaly Detection Algorithms (ADA) are readily available as services in various maintenance management systems. However, coping with the constantly changing stream data and the occurrence of idea drift in dynamic IoT environments poses challenges. Selecting an appropriate Anomaly Detection Service (ADS) in real-time becomes a complex task. To tackle this issue, the researchers devised a method for choosing and configuring ADS in real-time data anomaly detection. They employed the Tsfresh time-series feature extractor along with a feature selection strategy based on a genetic algorithm. This approach allowed for the rapid extraction of dominant features that effectively represented the patterns present in the stream data. Historical data, including stream data features and other relevant information, was gathered to facilitate the dynamic identification of crucial ADS at runtime.