## 7 Conclusions  
In this study, we introduce two significant contributions to the field of IoT security, i.e., Dragon_Pi, a diverse dataset containing normal and under-attack current traces from a DragonBoard 410c and a Raspberry Pi, as well as Dragon_Slice, an unsupervised convolutional autoencoder anomaly/intrusion detector for IoT based on this data.  
The CAE network in this investigation demonstrates two levels of robustness, one without dropout and another with dropout. The non-dropout network achieved a 0.7778 AUC without post-processing and 0.8764 AUC with a mean absolute filter (MAF) of length 16 on the network's mean squared error (MSE).  
Although this network exhibited nearly perfect normal class reconstruction, it proved overly skilled in reconstructing anomalies. Consequently, a more robust solution was sought to enhance the distinction between normal and anomalous unseen errors. Introducing dropout, aimed at reducing overfitting and incorporating identity mapping, yielded excellent results. The dropout network, with an AUC performance of 0.764 without post-processing and an AUC of 0.89 with a MAF on the MSE of length 17, not only outperformed the original network using the MAF-MSE but also demonstrated a significantly more robust output, with a more substantial gap in error between normal and anomalous classes due to its inability to reconstruct anomalies. This rendered the dropout network more reliable, trustworthy, and robust.  
This study leverages the explainable output of the CAE network to explain the featured AI. The AI's explanation revolves around reconstruction, the error of the input-output pair, and classification. The unique operational approach of reconstruction-based networks enhances the AI's explainability, allowing for a straightforward comparison between input and output. This facilitates understanding the network's decision-making process and provides insights into potential ways to enhance AI. The explainability of this AI assists in the involvement of humans in the AI loop within the system. In real-world scenarios, segments of interest classified by the AI can be referred to human experts for further analysis. How the network attempts to reconstruct the input can aid human experts in comprehending why a particular segment is deemed anomalous.  
Our future work on this topic will continue to investigate the overall subject of heterogeneous data types in IoT security. To this end, we will seek to create a new dataset, containing power consumption data as well as network traffic and device telemetry. This dataset, and the subsequent work thereon, will explore a methodology of incorporating power consumption data and other telemetry data into the pre-existing security pipelines. New anomaly detection architectures will be explored based on this dataset, with a focus on the heterogeneous nature of the dataset, allowing the use of a single model across not only multiple devices but also multiple data types.  
Methodology, D.L., D.-M.N. and E.P.; software, D.L.; formal analysis, D.L., D.-M.N., C.C.M. and E.P.; writing--original draft, D.L. and E.P.; writing--review and editing, A.T., C.C.M. and E.P.; supervision, C.C.M. and E.P.; project administration, E.P. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by a grant from the Science Foundation Ireland INSIGHT Centre for Data Analytics (grant number 12/RC/2289-P2), which is co-funded under the European Regional Development Fund.  
The data presented in this study is available online at [[https://doi.org/10.5281/zenodo.10784947](https://doi.org/10.5281/zenodo.10784947)]([https://doi.org/10.5281/zenodo.10784947](https://doi.org/10.5281/zenodo.10784947)) (accessed on 8 February 2024).  
The authors acknowledge the Insight SFI Research Centre for Data Analytics at University College Cork, Ireland. We would also like to acknowledge support from Qualcomm and Dell.  
The authors declare no conflicts of interest.