Malware detection in IOMT (MDI) using RNN-LSTM

Abstract

The Internet of Things (IoT) has recently emerged as a cutting-edge technology for creating smart environments. The Internet of Things (IoT) connects systems, applications, data storage, and services, which may be a new entry point for cyber-attacks as they provide continuous services in the organization. At the current time, software piracy and malware attacks pose significant threats to IoT security. These threats may grab vital information, causing economic and reputational harm. The Internet of Medical Things (IoMT) is a subset of the Internet of Things in which medical equipment exchanges highly confidential with one another. These advancements allow the healthcare industry to maintain a higher level of touch and care for its patients. Security is viewed as a significant challenge in any technology's reliance on the IoT. Remote hijacking, impersonation, denial of service attacks, password guessing, and man-in-the-middle are all security concerns. Critical data associated with IoT connectivity may be revealed, altered, or even rendered inaccessible to authenticated persons in the event of such attacks. the deep recurrent neural network is used to detect malicious infections in IoT networks by displaying color images. In this paper, we propose a method for detecting cyber-attacks on IoMT systems that tends to make use of innovative deep learning. Specifically, our method incorporates a set of long short-term memory (LSTM) modules into a detector ensemble using a recurrent neural network.