Designing Privacy-Preserving Machine Learning Frameworks for IOT Infrastructure Integration

Abstract

Developing machine learning frameworks that safeguard privacy while coordinating IoT infrastructure is fundamental to guaranteeing the protection and order of delicate data created by associated gadgets. In this work, we will examine concentrates on that have utilized machine learning (ML) to address IoT privacy issues and explore the benefits and disadvantages of involving data in ML-based IoT privacy draws near. We focus on utilizing machine learning (ML) models to identify malware in Internet of Things (IoT) gadgets, specifically ransomware, spyware, and tricky malware. We propose utilizing machine learning procedures to address privacy break location and test configuration maturing in the Internet of Things. The machine learning calculation is prepared to expect social designing. We talk about our review and assessment utilizing the "MalMemAssessment" datasets, which are focused on mimicking genuine privacy-related obfuscated malware. We mimic a few machine learning estimations to show their capacity to distinguish harmful attacks against privacy. The experimental examination shows that the proposed procedure has a serious level of accuracy and feasibility in identifying obfuscated and covered malware, outperforming cutting-edge strategies by 99.52%, and having expected utility in shielding an IoT network from malware. Test examination and findings are given exhaustively.