IoT Advancing Memory Dump Analysis: Integrating Machine Learning Techniques for Enhanced Malware Detection

The fast propagation of Internet of Things (IoT) devices has brought new vulnerabilities in cybersecurity to light. This is because standard techniques of malware detection often fail to meet expectations. This study's findings advocate using machine learning (ML) approaches in combination with memory dump analysis to improve the identification of malware inside the Internet of Things (IoT) ecosystem. Our technique seeks to detect harmful activity more accurately and efficiently by using machine learning algorithms to filter through memory dumps. This is particularly important when dealing with innovative or complex cyber threats. The revolutionary integration in question is expected to bring about a revolution in memory dump analysis. It will provide a more adaptable, precise, and scalable solution to protect the ever-growing network of Internet of Things devices from the ever-evolving risks associated with cybersecurity.