Details

Autor(en) / Beteiligte

• Adkisson, Mary
• Kimmell, Jeffrey C
• Gupta, Maanak
• Abdelsalam, Mahmoud

Titel

Autoencoder-based Anomaly Detection in Smart Farming Ecosystem

Ist Teil von

• 2021 IEEE International Conference on Big Data (Big Data), 2021, p.3390-3399

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Link zum Volltext

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• The inclusion of Internet of Things (IoT) devices is growing rapidly in all application domains. Smart Farming uses IoT devices to increase efficiency and optimize farming operations. These devices can be used in a cloud or edge computing infrastructure which can provide remote control of watering and fertilization, real time monitoring of farm conditions, and provide solutions for more sustainable practices. These improvements to efficiency and ease of use come with added risks to security and privacy. Combining vulnerable IoT devices with the critical infrastructure of the agriculture domain broadens the attack surface for adversaries. Cyberattacks in a large coordinated manner could disrupt the economy of agriculture-dependent nations. To the sensors in a system, an attack may appear as anomalous behaviour. Additionally, there are possibilities of anomalies generated due to faulty hardware, issues in network connectivity (if present), or simply abrupt changes to the environment due to weather, human error, or other unforeseen circumstances. To make these systems more secure, it is imperative to detect such data discrepancies and trigger appropriate mitigation mechanisms. In this paper, we propose an anomaly detection model for Smart Farming using an unsupervised Autoencoder machine learning model. We chose to use an Autoencoder as our method of anomaly detection because it attempts to reconstruct normal data with a low reconstruction loss and anomalous data with a high loss. The high reconstruction loss value for a data point indicates that the data is not like the rest. Our model was trained and tested on data collected from our greenhouse test-bed. Our proposed Autoencoder based anomaly detection method achieved 98.98% and took 262 seconds to train and has a detection time of .0585 seconds.