# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
## 6 Conclusions  
In this study, a novel FDIA detection scheme for AC in ISMG is suggested. While lots of work toward attacks and detections has been studied and investigated in DC model, rare research has concentrated on the AC peer, where numerous are accepted by ISMG. Current FDIA scheme focuses on FSVs technique. The FSVs method is combined FFT and SVD to elicit an auto feature to describe cyber-attack detection. Results defined FSVs as sensitive to abrupt variations in signals and able to detect FDIA in several conditions. It is proven that the suggested protection layout is a valid and rapid way in FDIA detection under various case studies. The FSVs can detect FDIA in voltage and current, accurately. The proposed scheme is able to distinguish FDIAs from common system operating status variations. To discern the efficiency of proposed FDIA detection scheme, a dynasty of universal simulations on 3-bus ISMG is accomplished. The suggested detector is able to obtain a great attack detection performance. Further, this detection mechanism outperforms the present FDIA detection methods with remarkable precise developments. It has been demonstrated that the offered protection technique is a reputable and quick style in FDI detection under different kinds of operating situations of ISMG. Hence, this study proposed an extremely precise and rapid detection pattern to secure the ISMGs in the face of FDIAs with detection precision above 96%. An extensive and general set of simulations have been executed on an AC-ISMG to appraise the proficiency and performance of the expressed FDI attack detection pattern under several kinds of attack precision.  
Assessing the performance of the proposed cyber-attack detection method on a real-time \"hardware experiment\" is able to be an interesting topic for future works. Additionally, the proposed method can perform on DC-MG and HVDC system as a hot topic. In addition, other cyber-attack methods such as data integrity attack, man in the middle attack, Distributed Denial-of-Service attack, etc., and also other detection methods such as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{3}{c}{**Genuine Value**} \\ \cline{3-4}  & **Technique** & & **Positives (\%)** & **Negatives (\%)** \\ \hline \multirow{6}{*}{Detection Pattern Response} & \multirow{2}{*}{FSVs} & Positives & 96.44 & 2.07 \\ \cline{3-4}  & & Negatives & 3.56 & 97.93 \\ \cline{2-4}  & & Positives & 96.38 & 4.58 \\ \cline{2-4}  & & Negatives & 3.62 & 95.42 \\ \cline{2-4}  & & Positives & 93.17 & 5.38 \\ \cline{2-4}  & & Negatives & 6.83 & 94.62 \\ \cline{2-4}  & & Positives & 89.47 & 9.93 \\ \cline{2-4}  & & Negatives & 10.53 & 90.07 \\ \hline \multicolumn{4}{c}{Technique} & Average Detection Time & DNN Training Time \\ \hline \multirow{6}{*}{Response Time} & FSVs & 10 ms & - \\ \cline{2-4}  & DNN with WT [32] & 3.51 ms & 2713.2 s \\ \cline{1-1} \cline{2-4}  & HHT [6] & 50 ms & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix results and comparisons.  
adaptive tuning threshold and deep learning with other signal processing methods can consider and investigate as new subjects.  
Conceptualization, Data curation, Formal analysis, Software, Investigation, Resources and Writing--original draft, M.D. and M.G.; Project administration, Supervision, T.N.; Validation, T.N., P.S., H.H.A., and A.A.-H.; Writing--review and editing; Visualization, Methodology and Funding acquisition, P.S., H.H.A., and A.A.-H. All authors contributed equally to this paper. This research paper is contributed by the authors mentioned above. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] and [PERSON] were supported by His Majesty's (HM) Fund and Sultan Qaboos University (SQU) under grant number SR/ENG/ECED/17/1.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.