# Systematic Review: Malware Detection and Classification in Cybersecurity
## 1 Introduction  
Malicious software, better known as malware, are programs that perform unwanted interventions within victim computers or devices [1]. Whether with or without the consent of the affected parties, malicious programs carry out harmful actions on computer systems, causing significant damage to the compromised machines. Understanding that malware is a key link in cyberattacks, the objective of these programs is to affect any of the three pillars of information: confidentiality, integrity, or availability of data. The impact has been so significant in recent years that science and business journals report that malware has cost the global economy trillions of dollars [1, 2]. In recent decades, we have witnessed a rapid growth and evolution in technology, radically changing the way we live and manage our digitalization. The proliferation of internet-connected devices, cloud services, the growth of Machine Learning (ML) [3], and the expansion of social networks have created a rich and complex digital environment. This new digital era has brought various benefits, ranging from greater global connectivityto large-scale business operations. However, it has also introduced a series of threats and vulnerabilities that target the confidentiality, integrity, and availability of information. Since the beginning of the pandemic, malware has increased significantly, with an estimated 1.5 billion new samples by 2020 [4]. This surge has impacted organizations, which have been forced to enhance their security measures. Malware is categorized by behavior, functionality, and propagation methods. Below are the main types of malware along with their characteristics:  
The attacks are not limited to individuals but also affect businesses of all kinds [5]. These attacks can have devastating consequences, including the theft of personal and financial data, significant operational disruptions, and severe reputational damage [6; 7]. Companies face constant challenges in protecting their systems and data from rapidly evolving cyber threats, such as ransomware, phishing, and software supply chain vulnerabilities [8; 9]. The challenge of securing new technologies mainly falls within the area of cybersecurity. This field is dedicated to protecting computer systems and everything related to them. Over the years, cybersecurity has been able to contain cyber threats by employing a sophisticated arsenal of techniques and methodologies that evolve to enhance their robustness and effectiveness. In recent years, the advancement of machine learning has stood out as a powerful tool for detecting cyberattacks [10; 11]. As shown in Figure 1, the number of publications on malware in cybersecurity has grown exponentially in the last 15 years (2010-2024). This increase reflects the need for new techniques and methodologies to address current problems, where the results of systematic searches reveal that malware represents a significant threat to today's technology [12; 13]. Without the ingenuity of cybercriminals to combine various methods, these attacks would not be possible. Additionally, human vulnerability plays a crucial role in the spread of these attacks [14; 15]. Machine Learning (ML) plays a fundamental role in malware detection and technological advancement. With the increasing sophistication of cyberattacks, it is becoming more difficult to detect and combat malware using traditional methods based on static rules. This is where machine learning becomes crucial, as it enables the development of algorithms and models capable of autonomously learning and adapting to new threats [16; 17]. Machine Learning-based malware detection systems can analyze large volumes of data and complex patterns, identifying malicious behaviors and anomalies that traditional approaches may miss. These systems continuously learn from exposure to new malware samples, improving their accuracy and detection capability over time [18; 19].