Abstract
In the past decade, rapid technological advances in the fields of electronics and telecommunications have given rise to versatile, ubiquitous decentralized embedded sensor systems with ad hoc wireless networking capabilities. Typically these systems are used to gather large amounts of data, while the detection of anomalies (such as system failures, intrusion, or unanticipated behavior of the environment) in the data (or other types or processing) is performed in centralized computer systems. In spite of the great interest that it attracts, the systematic porting and analysis of centralized anomaly detection algorithms to a decentralized paradigm (compatible with the aforementioned sensor systems) has not been thoroughly addressed in the literature. We approach this task from a new angle, assessing the viability of localized (in-node) anomaly detection based on machine learning. The main challenges we address are: (1) deploying decentralized, automated, online learning, anomaly detection algorithms within the stringent constraints of typical embedded systems; and (2) evaluating the performance of such algorithms and comparing them with that of centralized ones. To this end, we first analyze (and port) single and multi-dimensional input classifiers that are trained incrementally online and whose computational requirements are compatible with the limitations of embedded platforms. Next, we combine multiple classifiers in a single online ensemble. Then, using both synthetic and real-world datasets from different application domains, we extensively evaluate the anomaly detection performance of our algorithms and ensemble, in terms of precision and recall, and compare it to that of well-known offline, centralized machine learning algorithms. Our results show that the ensemble performs better than each individual decentralized classifier and that it can match the performance of the offline alternatives, thus showing that our approach is a viable solution to detect anomalies, even in environments with little a priori knowledge.