Important Feature Filtering in IoT Network Traffic to Enhance ML-based Classification
Abhishek Shukla, Rahul Gupta · 2024
In this continuously ever-evolving world every device is vulnerable to security threats, but the Internet of Things (IoT) environment necessitates early identification of threats. Real-time devices, such as home automation systems, and defense hardware are part of the Internet of Things (IoT), which may be accessed by hackers using open-source devices. Attackers can get access to network credentials, compromise network control, and start a new type of security breach. Thus, the problem with guaranteeing the credibility of network protection is the prompt detection of the growing number of complex malware assaults. To solve this, a significant volume of traffic data from the number of devices including both good and bad traffic is required. Here in this study, we are using some machine learning models for the detection of malware. We use NSL-KDD for the training and testing of the models.