Sensor Attack

Sensor attacks, targeting the data streams from various sensors in diverse systems (e.g., autonomous vehicles, IoT networks, power grids), aim to manipulate sensor readings for malicious purposes. Current research focuses on developing robust anomaly detection frameworks, often employing machine learning models like convolutional neural networks and Kalman filters, to identify compromised sensors and mitigate the impact of attacks. These efforts are crucial for enhancing the security and reliability of numerous critical systems, ranging from transportation to agriculture and energy infrastructure, by improving the accuracy and trustworthiness of sensor data. The development and validation of these methods often rely on real-world datasets and simulations to evaluate their effectiveness against various attack strategies.