A Hybrid Approach for IoT Sensor Anomaly Detection with LSTM and Autoencoder-Based Preprocessing

The rapid growth of IoT sensor networks has introduced new challenges in effectively detecting anomalies due to the continuous influx of high-dimensional, real-time data. This paper proposes an advanced anomaly detection framework that integrates enhanced data preprocessing techniques with Long Short-Term Memory (LSTM) networks for robust anomaly identification in IoT sensor data. The methodology includes rigorous data cleaning, normalization, and dimensionality reduction using autoencoders to ensure data quality and reduce noise. To further improve preprocessing, an Isolation Forest algorithm is employed for preliminary outlier detection, refining the input data for the LSTM model. The LSTM network is trained to capture long-term dependencies and temporal patterns, improving the accuracy of anomaly detection. Extensive experiments conducted on the Numenta Anomaly Benchmark (NAB) dataset demonstrate the effectiveness of the proposed approach, achieving high precision and recall. Comparative analysis with traditional GRU-based models highlights the superior performance of the LSTM architecture in handling temporal dependencies. This hybrid approach offers a scalable and efficient solution for real-time anomaly detection in IoT applications, enhancing the reliability of monitoring systems.