Forecasting Cyber-Attacks Using Machine Learning Models in IoT Environment

Abstract

Wildfires constitute a major environmental hazard, causing severe damage to forest ecosystems, biodiversity, infrastructure, and human life. Early and reliable wildfire detection is therefore essential to limit fire propagation and support rapid emergency response. However, conventional detection approaches based on human observation, satellite imagery, or fixed surveillance systems may suffer from delayed detection, limited coverage, and high operational costs. This study proposes an Internet of Things (IoT)-based intelligent framework for early wildfire detection in forest environments. The proposed system relies on distributed IoT sensor nodes to continuously monitor environmental parameters associated with fire occurrence, including temperature, relative humidity, smoke concentration, and gas levels. The collected sensor data are transmitted to a processing unit where \textbf{Machine Learning (ML) and Deep Learning (DL)} techniques are employed to distinguish normal environmental conditions from patterns potentially indicating an emerging wildfire. The proposed approach aims to provide continuous and automated forest monitoring while improving the reliability of early fire detection and reducing false alarms. Upon identifying abnormal environmental patterns associated with potential fire events, the system generates an early warning to support timely intervention by forest management and emergency response authorities. By combining IoT-based sensing with intelligent data analysis, this work provides a scalable framework for enhancing wildfire monitoring, early detection, and risk mitigation in forest ecosystems.