# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Swarm Intelligence  
Swarm Optimization is a complex machine learning technique that utilizes the collective intelligence of a group of cooperative agents to solve problems through evolutionary computations. In the realm of intrusion detection, [PERSON]. [37] explored the application of Swarm Intelligence (SI) approaches (2011). Their study involved a comprehensive analysis of various SI-based intrusion detection systems, examining their advantages and limitations. The researchers focused on developing supervised classification algorithms based on SI, with a particular emphasis on anomaly detection IDS. In SI-based IDS techniques, multiple agents collaborate and communicate with each other to address issues and find optimal solutions. In the context of abuse detection or anomaly detection clusters, agents can be deployed to determine classification criteria. The authors developed two groups of IDS, utilizing Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) techniques: (i) a system for intrusion detection based on Particle Swarm Optimization (PSM), and (ii) a system for intrusion detection based on Ant Colony Clustering (ACC). The inspiration for ACO algorithms stemmed from the behavior of ants in seeking the shortest paths from their nest to food sources.  
[PERSON] [38] introduced the first Ant Colony Optimization (ACO) technique for intrusion detection, utilizing directed graphs to model attacks. The collective movement dynamics observed in animal groups inspired the development of Particle Swarm Optimization (PSO) algorithms. The behavior of ants in gathering and sorting influenced the independent behavior of Ant Colony Clustering (ACC) algorithms. The authors also mentioned conducting a long-term study in their work. Swarm optimization offers several advantages, such as the adaptability of SI-based systems, allowing them to incorporate new data. Additionally, these systems are scalable since they utilize the same control architecture for a group of agents. They are also flexible, enabling the addition or removal of agents without necessitating changes to the overall architecture. However, SI-based systems also have some drawbacks: (i) The outcomes are unpredictable due to the intricacies of swarming. (ii) In a rich, hierarchical, swarm-based system, transitioning to states takes time. (iii) The system becomes redundant and uncontrolled as a result of the absence of centralized administration.  
The Ant-based Replication and MApping Protocol (ARMAP) is another bio-inspired approach for anomaly detection [39]. ARMAP is based on a grid information system. It uses swarm intelligence through the collective efforts of ant-like agents. Data, particularly metadata are collected on grid resource characteristic. Using local interactions, it is possible to achieve swarm intelligence at the global level. One important advantage is that ARMAP enforces dissemination of descriptors through high Quality of Service (QoS) resources.  
Swarm-based techniques such as ACO and PSO are important problem-solving techniques because they are based on decentralized coordination leveraging on collective intelligence. Swarm-based techniques also respond to dynamic environments and evolving data making them suitable for applications where conditions change rapidly [40]. Swarm-based techniques are becoming more important in emerging technologies like autonomous vehicles, and drone swarms where coordination and optimization are crucial.