###### Abstract
Considering that semi-supervised models are not solely reliant on labeled data, they can generalize better to unseen anomalies compared to supervised approaches. Based on the seminal efforts outlined above, this paper introduces a semi-supervised anomaly detection approach that combines side-channel energy consumption auditing specifically for robotic manipulators. The present study utilizes a small neural network, enabling its deployment on edge computing devices to perform anomaly detection tasks in real time. Moreover, only energy consumption profiles during operation are audited, making it less vulnerable to intentional intrusion. Our approach relies on a reconstruction network trained using only energy consumption profiles associated with normal operations. It then identifies anomalies by evaluating the magnitude of reconstruction errors, considering that abnormal patterns can cause large errors when reconstructed through a well-trained network. To this end, a static threshold is determined where samples with errors above the threshold are predicted as anomalies. The reconstruction network combines the well-known U-Net [16] architecture with a new Time-Frequency Fusion block. This block applies the Fast Fourier Transform to the time series data, providing complementary information and features in the frequency domain in addition to traditional time domain data processing. This delicate design enriches feature representation and improves detection performance. The main contributions of this work are enumerated as follows:  
1. Energy consumption serves as the sole data source that is acquired through a side monitoring channel for anomaly detection. This design mitigates the risk of targeted malicious attacks on measurement sensors and enhances the reliability of the proposed method. Notably, it also offers great potential for extension to other systems and applications. 2. A novel neural network block for Time-Frequency Fusion is introduced to develop a reconstruction network for anomaly detection. This new block leverages the Fast Fourier Transform to capture essential features in the frequency domain. This complementary knowledge facilitates the feature learning process by enriching feature representations, leading to improved detection performance, especially for anomalies with strong presentation in the frequency domain. 3. For this study, we compiled a tailored dataset documenting the energy consumption during the operational phases of a robotic manipulator. This dataset comprises over 6000 instances of energy consumption during normal operations, which were utilized to train the proposed model. Furthermore, more than 6000 instances of energy consumption incorporating simulated anomalies were collected for model verification purposes. The dataset has been made publicly available as an open-source resource to facilitating its utilization and evaluation by fellow researchers. The rest of this paper is structured as follows: Section 2 introduces prior works in related fields; Section 3 elaborates on the methodology and key research components; details regarding the experiments, including generation of the custom dataset, are outlined in Section 4; Section 5 presents the experimental results and analysis; finally, Section 6 concludes the paper with a brief technical summary.