Effective Cyber Attack Detection in an IoMT-Smart System using Deep Convolutional Neural Networks and Machine Learning Algorithms

The widespread availability of digital technology has reshaped the computing environment. being just one of them. pear phishing, impersonating, distributed denial of service stolen credentials, and man-in-the-middle attacks are just some of the many potential security concerns. Important information connected with an IoT connectivity might be stolen, changed, or unavailable to authorised users in the case of an attack. Because of this, protecting the IoT/IoMT ecosystem from malware has become an absolute need. To categorise and foresee new cyber threats, the primary purpose of this research is to show that a deep learning and surveillance machine learning models may be used in the context of IoMT IDS. In order to examine network data, it must first be standardised and cleaned. Then, in order to fine-tune the specifics, we resorted to a biologically inspired metaheuristic optimization technique. DCNN and other SML research is evaluated using state-of-the-art data for vulnerability detection.