Forecasting Cyber-Attacks using Machine learning Models in IoT Environment
Ahlem Bouafia, Amira Zrelli, Taoufik Aguili · 2023
Internet of Things (IoT) is a dynamic and evolving industry. IoT is a smart network that allows devices to exchange information and communicate with each other through internet. With IoT, human can achieve the purpose of tracking, monitoring, locating, identifying and managing things, today IoT security received considerable attention. The challenge of IoT security is exceedingly big and complicated, necessitating a huge variety of security solutions in order to provide high-level IoT security. The security of IoT applications will always need to morph and react to changes in several the application medicine, education, building . The tools in IOT networks differ based on the type of IoT application and its position in the ecosystem. Various cyber-security professionals are looking to artificial intelligence (AI) and more specifically, "deep learning" algorithms to dynamically secure IoT applications.In the case of this work, we treat particularly "smart buildings" security cyber-attacks. These methods can be utilized in intrusion detection.The proposed system uses machine learning algorithms to identify potential intruders and alert the system administrators of any suspicious activities. The goal of the system is to detect any potential malicious activities that could put smart building networks at risk.