## 5 Conclusions and Future Work  
The design of a Cross-Layerered framework for safeguarding the power grid's operation from physical component or communication network threats is described in this paper. To address power grid communication, we recommended adopting a distributed three-controller SDN architecture. We can manage our network with increased visibility, control, and responsiveness because of SDN. Moreover, using ONOS clusters eliminates the single point of failure that might occur when using a single controller. We benchmarked our proposed SDN framework against the conventional POX controller to demonstrate the network's increased performance and load management. SimComponents is used to quickly build and simulate DoS, Man-in-the-Middle (MiTM), and False Data Injection attacks (FDI) attacks. The state estimation is affected by FDI and DoS attacks, and all attacks have an impact on the communication network. To detect the corresponding attack, the state estimator and machine learning examine the consequences of all attacks. Simulink is used to represent the power grid, allowing for real-time simulation. SimComponent, a Python library, is used to simulate a communication network. To detect attacked samples, data from each layer is synced and evaluated using a real-time cross-layered machine learning technique. According to the results of our suggested architecture, a three-controller distributed arrangement outperforms a single controller by a factor of more than ten times the throughput. This allows for a greater flow of data throughout the network while reducing congestion caused by the processing constraints of a single controller. Moreover, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based algorithms in attack classification.  
In future work, we would like to extend this framework to include more types of cyber attacks, additional controllers, and P4 [37] and Stratum [38]-enabled switches. There are other cyber attacks we would like to defend against, such as ransomware, botnet, and host impersonation attacks. Furthermore, we would like to build upon our failure-resistant framework by adding additional standby controllers for each of the current controllers to increase protection against our model from unforeseen outages that may be experienced in the field. In addition, we would like to include P4 and Stratum-enabled switches/routers to allow for complete \"white box\" control of the forwarding devices. This would allow for the control of packet parsing at the forwarding device level to increase QoS in the network. The framework was developed in a real-time simulated environment, making it an ideal starting point for future research on data integrity cyber threats in smart grids.  
Conceptualization, D.A. and N.A.; methodology, D.A. and N.A.; software, D.A., N.A., R.M. and S.B.; validation, D.A., N.A., R.M. and S.B.; formal analysis, D.A., N.A., R.M. and S.B.; investigation, D.A., N.A., R.M. and S.B.; resources, A.B. and J.M.; data curation, A.B., K.N. and J.M.; writing--original draft preparation, D.A., N.A., R.M. and S.B.; writing--review and editing. A.B. and J.M.; visualization, A.B. and J.M.; supervision, A.B. and J.M.; project administration, A.B. and J.M.; funding acquisition, A.B. and J.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by NSF grant ECCS-1809739.  
Not applicable.  
The authors declare no conflict of interest.