# Malware API Calls Detection Using Hybrid Logistic Regression and RNN Model
## 1 Introduction  
The role of cybersecurity has become increasingly significant as cyberattacks have rapidly escalated, necessitating cutting-edge approaches to detecting and preventing malicious behavior [1; 2]. In recent years, the prevalence of malware has grown exponentially, posing a significant threat to the security of computer systems and networks worldwide. According to a report by AV-TEST, an independent IT security research institute, they registered over 1.2 billion malware samples by the end of 2021, with approximately 350,000 new malware samples being identified daily [3]. This staggering growth rate highlights the urgency of the development of advanced detection and prevention techniques to combat the ever-evolving malware landscape. The complexity and sophistication of malware attacks have also increased, with cybercriminals employing new tactics and techniques to evade traditional detection methods. A study conducted by Symantec revealed that targeted attacks increased by 42% in 2021, with ransomware attacks becoming more targeted and lucrative, and supply chain attacksincreasing by 93% [4]. These statistics emphasize the need for innovative, intelligent, and automated solutions to identify and counteract the growing threat posed by malware [5]. Malware, an abbreviation for \"malicious software\", refers to a diverse range of intrusive software programs designed with the intent to compromise the integrity, confidentiality, or availability of computer systems and their resources [6]. These malevolent software entities infiltrate, disrupt, or exploit computer systems, networks, or end-user devices, often leading to unauthorized access, data theft, or corruption [7]. Malware encompasses a broad spectrum of software types, including viruses, worms, Trojans, ransomware, adware, spyware, and rootkits, each exhibiting unique characteristics and methods of propagation [8]. The proliferation of malware poses a significant threat to both individuals and organizations, necessitating continuous advancements in detection and prevention techniques, to safeguard the digital ecosystem [9]. Malware analysis is a critical aspect of cybersecurity that involves systematically examining, understanding, and dissection malicious software. By analyzing malware, researchers and cybersecurity professionals can gain valuable insights into its functionality, purpose, and potential impact on computer systems, networks, and end-user devices [10]. This process plays a pivotal role in enhancing the overall security position of organizations and individuals alike. The primary objectives of malware analysis include identifying the nature and characteristics of the malware, such as its type (e.g., virus, worm, Trojan), the method of propagation, and its behavior within the infected system [9]. These insights enable the development of effective countermeasures, including antivirus signatures, intrusion detection system (IDS) rules, or software patches that can detect, prevent, or remediate infections caused by malware [7]. Moreover, malware analysis helps strengthen cybersecurity defenses by providing a deeper understanding of the tactics, techniques, and procedures (TTPs) employed by cybercriminals. This knowledge facilitates the development of proactive security measures and strategies to mitigate future threats [6]. Additionally, the information gleaned from malware analysis can support incident response and digital forensic investigations by attributing the malware to specific threat actors, understanding their motives and objectives, and facilitating the collection of evidence for legal or law enforcement purposes [11]. Malware analysis techniques typically fall into two categories: static analysis and dynamic analysis. Static analysis involves examining the malware's code and metadata without executing it, while dynamic analysis entails running the malware in a controlled and isolated environment, to observe its behavior [10].