Internet of Things Anomaly Detection using Machine Learning

In recent years, an increasing number of devices are being connected to the Internet that encompasses more than just traditional devices. Internet of Things integrates real-world sensors such as smart devices or environment sensors with the Internet allowing for real}-time monitoring of conditions. IoT devices are often constrained in their resources as the sensors involved are designed for specific purposes. Due to these constraints, typical methods of intrusion and anomaly detection cannot be used. Also, due to the amount of raw input data from these sensors, detecting anomalies among the noise and other background data can be computationally intensive. A possible solution to this is by using machine learning models that are trained on both normal and abnormal behavior to detect when anomalies occur. By using techniques such as autoencoders, models can be trained that have learned normal operating conditions. In this study, we explore the use of machine learning techniques such as autoencoders to effectively handle the high dimensionality of sensor datasets while consequently learning their normal operating conditions. Autoencoders are a type of neural network which attempts to reconstruct its input data by combining two NNs, an encoder, and a decoder network. The encoder learns its input by encoding it into a lower-dimensional space while capturing the interactions and correlations between variables. In this paper, we explore the use of techniques such as autoencoders to create a lower-dimensional representation of high dimensional sensor input. Autoencoders encode the data allowing for the network to learn the interactions between parameters in normal conditions which when reconstructed with the decoder represents non-anomalous behavior. When data containing anomalies are input into the network errors will occur within the reconstruction. The error between the reconstructions can be measured using a distance function to determine if an observation is anomalous.