# Mdip1
## 6 Conclusions and Future Work  
The findings of this study highlight the potential risks that IoT device membership inference attacks (MIA) pose to the privacy of IoT devices. While there has been some research on protecting privacy in IoT systems, there is still a lack of focus on protecting privacy in IoT systems. Figure 14: Confusion matrix of anomaly detection using perturbed and transformed data, with dark purple representing correctly detection instances and light blue representing incorrectly detection instances. (**a**) Raw data; (**b**) Perturbed data; (**c**) Transformed data. device-level privacy-revealing features. As a result, this work proposes a novel method to safeguard the privacy of IoT devices by preventing adversaries from inferring their types. One of the main challenges in developing a privacy protection method for IoT devices is to find a balance between preserving the inherent value of the original IoT data while restricting what the attacker can learn. This work has addressed this challenging problem by proposing a time series data transformation method that effectively reshapes IoT packet data while preserving the important features and ensuring that the transformed dataset still contains useful information. The proposed time series data transformation method appears to be a cost-effective and efficient solution to mitigate device-level vulnerability. The results of the study demonstrate that the transformed dataset retains the intrinsic value of the original IoT data while preserving data utility. This means that the transformed dataset can still be used for various applications, such as anomaly detection, without revealing sensitive information about the device type. Overall, the proposed solution is an important step towards safeguarding the privacy of IoT devices from membership inference attacks. Further research can be conducted to evaluate the effectiveness of the proposed method in different IoT environments and to optimize the method for larger-scale IoT systems. We plan to incorporate the TON_IoT datasets [41] as part of our evaluation of the data utility resulting from our transformation method. These datasets are obtained from UNSW and comprise both Industry 4.0/Internet of Things (IoT), and Industrial IoT (IIoT) datasets, providing us with an opportunity to further assess the effectiveness of our transformation method. While the proposed data transformation method appears to be effective in mitigating device-level vulnerability in IoT systems, there is still a need to explore other transformation methods. One of the limitations of the LSTM-based transformer used in this study is that it operates as a blackbox, which means that the granularity of changing privacy-revealing features is not controllable. This lack of control over the transformation process could potentially lead to the loss of important information or features that could be useful for other applications. To address this limitation, future research can explore the use of other time series transformers that provide more control over the transformation process. One area for further exploration is the use of time series transformers [42] to convert time series data. Time series data are a critical component of IoT systems, and the use of time series transformers could offer a more effective way to preserve the privacy of IoT devices. Time series transformers are specifically designed to handle time series data, which could make them more effective at preserving the important features of IoT data while preventing adversaries from inferring device types. In conclusion, while the proposed data transformation method is an important step towards protecting the privacy of IoT devices, further research is needed to explore other transformation methods that provide more control over the transformation process. The use of time series transformers is a promising area for future exploration, as it could offer a more effective way to preserve the privacy of IoT devices, especially when dealing with time series data.