Abstract:
The sudden proliferation of the Internet of Things (IoT) has brought more convenience
and functionality than ever before to healthcare, education, industry, and other
aspects of life; it has also created major cybersecurity risks. Of these, ransomware has
become a serious menace with the ability to encrypt information or even paralyze the
devices until a ransom is paid. Especially, IoT devices can be compromised because of
their limited computing resources, heterogeneity, and the use of traditional security
approaches that, in most cases, cannot cope with new threats. The paper will discuss
the essence and effects of ransomware attacks on IoT devices, especially mobile devices
and interconnected networks. We analyze ransomware dissemination processes,
malware behavior, and the implications of such processes on the performance and
integrity of the device. Besides, the paper assesses existing detection, monitoring, and
mitigation plans, such as behavioral analysis, intrusion prevention systems, and end-
user awareness programs. The study outlines the shortcomings of conventional
security measures in IoT systems and highlights the need to have elaborate,
customized security models. These frameworks will technically address the risk of
ransomware by combining technical defenses and active user education to ensure risk
reduction, safety of sensitive information, and the reliable and secure functioning of
IoT ecosystems. We used nine machine learning models, and the neural network had
a high accuracy of 98%. The results are critical to researchers, practitioners, and
policymakers who aim to find effective processes of overcoming the ransomware
threats in the networks of connected devices.
