  After decades of research, the Internet of Things (IoT) is finally permeating
real-life and helps improve the efficiency of infrastructures and processes as
well as our health. As a massive number of IoT devices are deployed, they
naturally incur great operational costs to ensure intended operations. To
effectively handle such intended operations in massive IoT networks, automatic
detection of malfunctioning, namely anomaly detection, becomes a critical but
challenging task. In this paper, motivated by a real-world experimental IoT
deployment, we introduce four types of wireless network anomalies that are
identified at the link layer. We study the performance of threshold- and
machine learning (ML)-based classifiers to automatically detect these
anomalies. We examine the relative performance of three supervised and three
unsupervised ML techniques on both non-encoded and encoded (autoencoder)
feature representations. Our results demonstrate that; i) selected supervised
approaches are able to detect anomalies with F1 scores of above 0.98, while
unsupervised ones are also capable of detecting the said anomalies with F1
scores of, on average, 0.90, and ii) OC-SVM outperforms all the other
unsupervised ML approaches reaching at F1 scores of 0.99 for SuddenD, 0.95 for
SuddenR, 0.93 for InstaD and 0.95 for SlowD.
