A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data
This work addresses the challenge of scarce labeled data for anomaly detection in time-series IoT traffic, which is crucial for service quality, security, and financial loss prevention, representing an incremental improvement over prior methods that still relied on some labeled data.
The paper tackled the problem of anomaly detection in multivariate IoT traffic data by proposing a transfer learning model that requires no labeled data in either source or target domains, achieving higher accuracy than existing techniques in empirical evaluations on novel intrusion detection datasets.
In recent years, rapid technological advancements and expanded Internet access have led to a significant rise in anomalies within network traffic and time-series data. Prompt detection of these irregularities is crucial for ensuring service quality, preventing financial losses, and maintaining robust security standards. While machine learning algorithms have shown promise in achieving high accuracy for anomaly detection, their performance is often constrained by the specific conditions of their training data. A persistent challenge in this domain is the scarcity of labeled data for anomaly detection in time-series datasets. This limitation hampers the training efficacy of both traditional machine learning and advanced deep learning models. To address this, unsupervised transfer learning emerges as a viable solution, leveraging unlabeled data from a source domain to identify anomalies in an unlabeled target domain. However, many existing approaches still depend on a small amount of labeled data from the target domain. To overcome these constraints, we propose a transfer learning-based model for anomaly detection in multivariate time-series datasets. Unlike conventional methods, our approach does not require labeled data in either the source or target domains. Empirical evaluations on novel intrusion detection datasets demonstrate that our model outperforms existing techniques in accurately identifying anomalies within an entirely unlabeled target domain.