# Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
## 5 Discussion and Conclusion  
Protecting smart grids from cyberattacks is paramount in regard to ensuring the reliability and safety of grid operations. By deploying robust detection mechanisms and response strategies, operators can mitigate the impact of cyber threats and safeguard the grid infrastructure. The study demonstrates the effectiveness of the AE-GRU-based anomaly detection methods in enhancing the cybersecurity of smart grid networks using the BUT-IEC104-I dataset. The advantages of this approach include its ability to accurately detect the various types of anomalies, such as DOS attacks, switching attacks, injection command attacks, and connection loss attacks. The integration of the AE-GRU models with the semi-supervised anomaly detection methods, such as the EE and LOF methods, enhances the adaptability and robustness, resulting in an improved detection performance across the different attack scenarios. This highlights our framework's efficacy and the benefits of integrating the AE-GRU with semi-supervised anomaly detection to identify the cyber threats in smart grid traffic.  
Despite the satisfactory detection performance, the study also has limitations that warrant attention. One limitation is the reliance on simulated attack scenarios, which may not fully capture the complexity and variability of real-world cyber threats. Additionally, the performance of the proposed framework may be influenced by factors such as the quality and quantity of training data, as well as the specific configurations of the smart grid network under consideration. Several potential solutions can be considered to enhance the performance of the proposed framework. Firstly, increasing the diversity and authenticity of the training data by incorporating real-world network traffic traces can improve the robustness of the AE-GRU models.  
The encouraging outcomes (performance) of the presented cyberattack detection solution motivate further exploration and potential integration into the monitoring and intrusion detection systems across diverse industrial domains. Moreover, future investigations will be conducted by incorporating wavelet techniques to handle noisy data. Additionally, we intend to investigate the effectiveness of joining (merging) unsupervised deep learning with statistical monitoring charts, such as the Generalized Likelihood Ratio Test. Another potential line of improvement is to explore techniques such as transfer learning or domain adaptation that could help leverage the knowledge from the related domains to improve the generalization and adaptability of the AE-GRU models to diverse smart grid environments. Moreover, incorporating the ensemble learning approaches, where multiple models are combined to make predictions, could potentially improve the overall robustness and reliability of the anomaly detection system.  
Figure 10: Average performance metrics of the considered anomaly detection methods.  
**Author Contributions:** F.H.: Writing--Review and editing, Formulation, Methodology, Investigation, Validation; B.B.: Writing, Review and editing, Conceptualization, Validation; A.D.: Writing--Review and editing, Methodology, Software, Formal analysis, Validation; Y.S.: Review and Editing, Formal analysis, Supervision, Visualization. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data used in this study are available at [[https://ieee-dataport.org/documents/ics-dataset-smart-grid-anomaly-detection](https://ieee-dataport.org/documents/ics-dataset-smart-grid-anomaly-detection)]([https://ieee-dataport.org/documents/ics-dataset-smart-grid-anomaly-detection](https://ieee-dataport.org/documents/ics-dataset-smart-grid-anomaly-detection)) (accessed on 20 May 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.