Assessment of IOT Devices with Collaborative Intrusion Detection System Using Deep Learning Techniques
B. Archana, K. Srujan Raju, Rajesh Tiwari, Maheedhar Vara Prasad · 2024
Although the Internet of Things (IoT) and its uses make life easier, many researchers are very interested in them. Assaults against those devices, such as denial-of-service and sybil assaults, have significantly grown because to their widespread awareness, potentially making the system unusable. Consequently, it is now required that the method for identifying malware in the Internet of Things be used. In this study, an integrated intrusion detection system (IDS) called CNN-IoT is proposed. Its purpose is for monitoring IoT devices for harmful activities. The suggested approach was assessed using the NSW-NB15 dataset. 98.54% accuracy was achieved attained using a type II inaccuracy rate of about 0.01. The suggested strategy operates better than the other current approaches that are documented in the research, according to preliminary assessments.