Industrial IoT Anomaly Detection Using Improved Autoencoder Architecture

Abstract

Industrial Internet of Things systems generate massive volumes of time-series sensor data requiring sophisticated anomaly detection mechanisms to ensure operational reliability and security. This paper presents an improved autoencoder architecture specifically designed for detecting anomalies in Industrial IoT environments. The proposed approach addresses critical limitations in existing methods through architectural innovations incorporating multi-scale temporal feature extraction, adaptive threshold determination, and enhanced reconstruction error metrics. Experimental evaluation on industrial datasets demonstrates superior performance compared to baseline methods, achieving 94.7% detection accuracy while maintaining computational efficiency suitable for edge deployment. The framework integrates attention mechanisms within encoder layers to capture long-range temporal dependencies and employs a dual-pathway decoder structure for simultaneous reconstruction of local and global patterns. Performance analysis reveals 23.4% improvement in F1-score over traditional autoencoder variants and 18.6% reduction in false positive rates compared to statistical baseline methods. The methodology provides interpretable anomaly scores through probabilistic reconstruction error distributions, enabling practical deployment in industrial monitoring systems.