Machine Learning-Based Malware Detection System for Android Operating Systems

Malware, a term derived from malicious software, includes any specially designed software that provides unauthorized access to computer systems and networks to disrupt devices. It assumes a critical role in emphasizing the significance of security within Android operating systems. As our world increasingly depends on smartphones for diverse activities, including communication, banking, and accessing sensitive information, the potential risks posed by malware grow more pronounced. Android devices can fall victim to the infiltration of malicious software, resulting in compromised user privacy, personal data theft, and financial harm. The prevalence of malware serves as a powerful reminder that robust security measures are indispensable for Android systems. It compels users and developers to remain vigilant, continuously update their devices, and employ effective antivirus and anti-malware solutions. By comprehending the potential dangers associated with malware, users can adopt safe browsing practices, steer clear of suspicious downloads, and safeguard their devices, ensuring a secure and dependable Android experience. Machine learning (ML) assumes a pivotal role in the realm of malware detection, delivering significant benefits and advancements in cybersecurity. In this study, we have developed a machine learning–based malware detection system that exhibits enhanced detection accuracy, adaptive and dynamic protection mechanisms, and improved zero-day threat detection. According to the experimental results of the research conducted, it shows the efficiency of the proposed models.