Research Article
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Year 2022, Volume: 11 Issue: 4, 1 - 13, 31.12.2022

Kübra Bitirgen Ümmühan Başaran Filik

Abstract

References

  • U. Adhikari, T. Morris, and S. Pan, “Wams cyber-physical test bed for power system, cybersecurity study, and data mining,” IEEE Trans. Smart Grid, vol. 8, no. 6, pp. 2744–2753, 2016.
  • G. Dileep, “A survey on smart grid technologies and applications,” Renew. Energy, vol. 146, pp. 2589–2625, 2020.
  • M. A. Hasnat and M. Rahnamay-Naeini, “A graph signal processing framework for detecting and locating cyber and physical stresses in smart grids,” IEEE Trans. Smart Grid, vol. 13, no. 5, pp. 3688–3699, 2022.
  • P. Shaw and M. K. Jena, “A novel event detection and classification scheme using wide-area frequency measurements,” IEEE Trans. Smart Grid, vol. 12, no. 3, pp. 2320–2330, 2020.
  • P. S. R. Committee et al., “IEEE guide for phasor data concentrator requirements for power system protection, control, and monitoring,” IEEE: Piscataway, NJ, USA, 2013.
  • Y. Chakhchoukh, H. Lei, and B. K. Johnson, “Diagnosis of outliers and cyber attacks in dynamic PMU-based power state estimation,” IEEE Trans. Power Syst., vol. 35, no. 2, pp. 1188– 1197, 2019.
  • S. Siamak, M. Dehghani, and M. Mohammadi, “Dynamic GPS spoofing attack detection, localization, and measurement correction exploiting PMU and SCADA,” IEEE Syst. J., vol. 15, no. 2, pp. 2531–2540, 2020.
  • S. Sahoo, T. Dragicevi ˇ c, and F. Blaabjerg, “Cyber security ´ in control of grid-tied power electronic converters—challenges and vulnerabilities,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 9, no. 5, pp. 5326–5340, 2019.
  • K. Bitirgen and U. B. Filik, “A hybrid deep learning model for ¨ discrimination of physical disturbance and cyber-attack detection in smart grid,” Int. J. Crit. Infrastruct. Prot., p. 100582, 2022.
  • K. Manandhar, X. Cao, F. Hu, and Y. Liu, “Detection of faults and attacks including false data injection attack in smart grid using kalman filter,” IEEE Trans. Control Netw. Syst., vol. 1, no. 4, pp. 370–379, 2014.
  • D. Ye and T.-Y. Zhang, “Summation detector for false datainjection attack in cyber-physical systems,” IEEE Trans. Cybern., vol. 50, no. 6, pp. 2338–2345, 2019.
  • R. Franco, C. Sena, G. N. Taranto, and A. Giusto, “Using synchrophasors for controlled islanding-A prospective application for the Uruguayan power system,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 2016–2024, 2012.
  • T. M. L. Assis and G. N. Taranto, “Automatic reconnection from intentional islanding based on remote sensing of voltage and frequency signals,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1877–1884, 2012.
  • Schweitzer Engineering Laboratories, “Synchrophasors.” Accessed Sept. 18, 2022. [Online]. Available: https: //selinc.com/solutions/synchrophasors/
  • Arbiter Systems, “Arbiter 1133A.” Accessed Sept. 18, 2022. [Online]. Available: https://www.arbiter.com/catalog/product/ model-1133a-power-sentinel.php#tabs-2
  • T. T. Kim and H. V. Poor, “Strategic protection against data injection attacks on power grids,” IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 326–333, 2011.
  • R. Sodhi, S. Srivastava, and S. Singh, “Multi-criteria decisionmaking approach for multi-stage optimal placement of phasor measurement units,” IET Gener. Transm. Distrib., vol. 5, no. 2, pp. 181–190, 2011.
  • G. Khare, A. Mohapatra, and S. Singh, “A real-time approach for detection and correction of false data in PMU measurements,” Electr. Power Syst. Res., vol. 191, p. 106866, 2021.
  • B. Sikdar and J. H. Chow, “Defending synchrophasor data networks against traffic analysis attacks,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 819–826, 2011.
  • T. H. Morris, S. Pan, and U. Adhikari, “Cyber security recommendations for wide area monitoring, protection, and control systems,” in 2012 IEEE Pow. Ener. Soc., 2012, pp. 1–6.
  • Y. Wang, M. M. Amin, J. Fu, and H. B. Moussa, “A novel data analytical approach for false data injection cyber-physical attack mitigation in smart grids,” IEEE Access, vol. 5, pp. 26 022– 26 033, 2017.
  • J. Wei and G. J. Mendis, “A deep learning-based cyber-physical strategy to mitigate false data injection attack in smart grids,” in 2016 Joint Workshop on Cyber-Physical Security and Resilience in Smart Grids (CPSR-SG). IEEE, 2016, pp. 1–6.
  • M.-J. Kang and J.-W. Kang, “Intrusion detection system using deep neural network for in-vehicle network security,” PloS one, vol. 11, no. 6, p. e0155781, 2016.
  • T. Ma, F. Wang, J. Cheng, Y. Yu, and X. Chen, “A hybrid spectral clustering and deep neural network ensemble algorithm for intrusion detection in sensor networks,” Sensors, vol. 16, no. 10, p. 1701, 2016.
  • O. Kosut, L. Jia, R. J. Thomas, and L. Tong, “Malicious data attacks on the smart grid,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 645–658, 2011.
  • L. Liu, M. Esmalifalak, Q. Ding, V. A. Emesih, and Z. Han, “Detecting false data injection attacks on power grid by sparse optimization,” IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 612– 621, 2014.
  • R. Deng, G. Xiao, R. Lu, H. Liang, and A. V. Vasilakos, “False data injection on state estimation in power systems—attacks, impacts, and defense: A survey,” IEEE Trans. Industr. Inform., vol. 13, no. 2, pp. 411–423, 2016.
  • M. Liao, D. Shi, Z. Yu, Z. Yi, Z. Wang, and Y. Xiang, “An alternating direction method of multipliers based approach for pmu data recovery,” IEEE Trans. Smart Grid, vol. 10, no. 4, pp. 4554–4565, 2018.
  • J. Zhao, L. Mili, and M. Wang, “A generalized false data injection attacks against power system nonlinear state estimator and countermeasures,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 4868–4877, 2018.
  • Mississippi State University Critical Infrastructure Protection Center, “Industrial Control System Cyber Attack Data Set.” Accessed Sept. 18, 2022. [Online]. Available: http: //www.ece.msstate.edu/wiki/index.php/ICS-Attack-Dataset
  • R. C. B. Hink, J. M. Beaver, M. A. Buckner, T. Morris, U. Adhikari, and S. Pan, “Machine learning for power system disturbance and cyber-attack discrimination,” in 2014 7th International symposium on resilient control systems (ISRCS). IEEE, 2014, pp. 1–8.
  • H. Hoffmann, “Kernel PCA for novelty detection,” Pattern Recognit., vol. 40, no. 3, pp. 863–874, 2007.
  • F. Meng, Y. Fu, and F. Lou, “A network threat analysis method combined with kernel PCA and LSTM-RNN,” in 2018 Tenth Int. Conf. Adv. Comput. Intel. (ICACI). IEEE, 2018, pp. 508– 513.
  • F. Meng, Y. Fu, F. Lou, and Z. Chen, “An effective network attack detection method based on kernel PCA and LSTMRNN,” in 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC). IEEE, 2018, pp. 568–572.
  • D. B. Rubin and R. J. Little, Statistical analysis with missing data. John Wiley & Sons, 2019.
  • T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago, and O. Tabona, “A survey on missing data in machine learning,” J. Big Data, vol. 8, no. 1, pp. 1–37, 2021.
  • M. Esmalifalak, L. Liu, N. Nguyen, R. Zheng, and Z. Han, “Detecting stealthy false data injection using machine learning in smart grid,” IEEE Syst. J., vol. 11, no. 3, pp. 1644–1652, 2014.
  • J. Sakhnini, H. Karimipour, and A. Dehghantanha, “Smart grid cyber attacks detection using supervised learning and heuristic feature selection,” in 2019 IEEE 7th international conference on smart energy grid engineering (SEGE), 2019, pp. 108–112.
  • M. Hasan, M. M. Islam, M. I. I. Zarif, and M. Hashem, “Attack and anomaly detection in iot sensors in iot sites using machine learning approaches,” IEEE Internet Things J., vol. 7, p. 100059, 2019.
  • P. K. Jena, S. Ghosh, E. Koley, and M. Manohar, “An ensemble classifier based scheme for detection of false data attacks aiming at disruption of electricity market operation,” J. Netw. Syst. Manag., vol. 29, no. 4, pp. 1–26, 2021.
  • N. Farnaaz and M. Jabbar, “Random forest modeling for network intrusion detection system,” Procedia Comput. Sci., vol. 89, pp. 213–217, 2016.
  • J. Waring, C. Lindvall, and R. Umeton, “Automated machine learning: Review of the state-of-the-art and opportunities for healthcare,” Artif. Intell. Med., vol. 104, p. 101822, 2020.
  • A. Tabakhpour and M. M. Abdelaziz, “Neural network model for false data detection in power system state estimation,” in 2019 IEEE Can. Conf. Electr. Comput. Eng. (CCECE). IEEE, 2019, pp. 1–5. S. Basumallik, R. Ma, and S. Eftekharnejad, “Packet-data anomaly detection in PMU-based state estimator using convolutional neural network,” Int. J. Electr. Power Energy Syst., vol. 107, pp. 690–702, 2019.
  • A. Sayghe, J. Zhao, and C. Konstantinou, “Evasion attacks with adversarial deep learning against power system state estimation,” in 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020, pp. 1–5.
  • C. Konstantinou and M. Maniatakos, “A data-based detection method against false data injection attacks,” IEEE Des. Test, vol. 37, no. 5, pp. 67–74, 2019.
  • M. Ozay, I. Esnaola, F. T. Y. Vural, S. R. Kulkarni, and H. V. Poor, “Machine learning methods for attack detection in the smart grid,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 8, pp. 1773–1786, 2015.
  • L. Cai, N. F. Thornhill, S. Kuenzel, and B. C. Pal, “Widearea monitoring of power systems using principal component analysis and k-nearest neighbor analysis,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 4913–4923, 2018.
  • S. Manocha, V. Bansal, I. Kaushal, and A. Bhat, “Efficient power theft detection using smart meter data in advanced metering infrastructure,” in 2020 4th Int. Conf. Intell. Comput. Control Syst. (ICICCS). IEEE, 2020, pp. 765–770.
  • B. C. Costa, B. L. Alberto, A. M. Portela, W. Maduro, and E. O. Eler, “Fraud detection in electric power distribution networks using an ann-based knowledge-discovery process,” Int. J. Artif. Intell., vol. 4, no. 6, p. 17, 2013.
  • M. N. Hasan, R. N. Toma, A.-A. Nahid, M. M. Islam, and J.-M. Kim, “Electricity theft detection in smart grid systems: A CNN-LSTM based approach,” Energies, vol. 12, no. 17, p. 3310, 2019.

Performance Analysis of PCA Based Machine Learning Approaches on FDIA Detection

Year 2022, Volume: 11 Issue: 4, 1 - 13, 31.12.2022

Kübra Bitirgen Ümmühan Başaran Filik

Abstract

Smart grid (SG) and its specific structures are widely taken notice of by many researchers studying power systems. This paper compares and analyzes the performance of five machine learning approaches combined with principal component analysis (PCA) to do the task of false data injection attack (FDIA) detection of an SG. For this purpose, PCA method combinations are presented and tested by using labeled data. Phasor measurement unit (PMU) data is a critical source of monitoring of progress and performance of an SG system. PMUs are perniciously influenced by FDIAs trying to manipulate the measurements without being noticed by the bad data detector (BDD) of the SG system. In one sense, the selected PMU data consisting of various features which play an important role in the control system of SG is used to analyze the characteristics of the SG system. The results show that FDIA detection is effectively accomplished. The efficiency of the proposed hybrid PCA-based various machine learning approaches is illustrated on a real measured PMU dataset. As empirical results show, Random Forest (RF) with PCA achieves the entire accuracy of 95% in FDIA detection.

Keywords

False data injection attack (FDIA) Phasor measurement unit (PMU) Principle component analysis (PCA) Smart grid (SG)

References

  • U. Adhikari, T. Morris, and S. Pan, “Wams cyber-physical test bed for power system, cybersecurity study, and data mining,” IEEE Trans. Smart Grid, vol. 8, no. 6, pp. 2744–2753, 2016.
  • G. Dileep, “A survey on smart grid technologies and applications,” Renew. Energy, vol. 146, pp. 2589–2625, 2020.
  • M. A. Hasnat and M. Rahnamay-Naeini, “A graph signal processing framework for detecting and locating cyber and physical stresses in smart grids,” IEEE Trans. Smart Grid, vol. 13, no. 5, pp. 3688–3699, 2022.
  • P. Shaw and M. K. Jena, “A novel event detection and classification scheme using wide-area frequency measurements,” IEEE Trans. Smart Grid, vol. 12, no. 3, pp. 2320–2330, 2020.
  • P. S. R. Committee et al., “IEEE guide for phasor data concentrator requirements for power system protection, control, and monitoring,” IEEE: Piscataway, NJ, USA, 2013.
  • Y. Chakhchoukh, H. Lei, and B. K. Johnson, “Diagnosis of outliers and cyber attacks in dynamic PMU-based power state estimation,” IEEE Trans. Power Syst., vol. 35, no. 2, pp. 1188– 1197, 2019.
  • S. Siamak, M. Dehghani, and M. Mohammadi, “Dynamic GPS spoofing attack detection, localization, and measurement correction exploiting PMU and SCADA,” IEEE Syst. J., vol. 15, no. 2, pp. 2531–2540, 2020.
  • S. Sahoo, T. Dragicevi ˇ c, and F. Blaabjerg, “Cyber security ´ in control of grid-tied power electronic converters—challenges and vulnerabilities,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 9, no. 5, pp. 5326–5340, 2019.
  • K. Bitirgen and U. B. Filik, “A hybrid deep learning model for ¨ discrimination of physical disturbance and cyber-attack detection in smart grid,” Int. J. Crit. Infrastruct. Prot., p. 100582, 2022.
  • K. Manandhar, X. Cao, F. Hu, and Y. Liu, “Detection of faults and attacks including false data injection attack in smart grid using kalman filter,” IEEE Trans. Control Netw. Syst., vol. 1, no. 4, pp. 370–379, 2014.
  • D. Ye and T.-Y. Zhang, “Summation detector for false datainjection attack in cyber-physical systems,” IEEE Trans. Cybern., vol. 50, no. 6, pp. 2338–2345, 2019.
  • R. Franco, C. Sena, G. N. Taranto, and A. Giusto, “Using synchrophasors for controlled islanding-A prospective application for the Uruguayan power system,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 2016–2024, 2012.
  • T. M. L. Assis and G. N. Taranto, “Automatic reconnection from intentional islanding based on remote sensing of voltage and frequency signals,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1877–1884, 2012.
  • Schweitzer Engineering Laboratories, “Synchrophasors.” Accessed Sept. 18, 2022. [Online]. Available: https: //selinc.com/solutions/synchrophasors/
  • Arbiter Systems, “Arbiter 1133A.” Accessed Sept. 18, 2022. [Online]. Available: https://www.arbiter.com/catalog/product/ model-1133a-power-sentinel.php#tabs-2
  • T. T. Kim and H. V. Poor, “Strategic protection against data injection attacks on power grids,” IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 326–333, 2011.
  • R. Sodhi, S. Srivastava, and S. Singh, “Multi-criteria decisionmaking approach for multi-stage optimal placement of phasor measurement units,” IET Gener. Transm. Distrib., vol. 5, no. 2, pp. 181–190, 2011.
  • G. Khare, A. Mohapatra, and S. Singh, “A real-time approach for detection and correction of false data in PMU measurements,” Electr. Power Syst. Res., vol. 191, p. 106866, 2021.
  • B. Sikdar and J. H. Chow, “Defending synchrophasor data networks against traffic analysis attacks,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 819–826, 2011.
  • T. H. Morris, S. Pan, and U. Adhikari, “Cyber security recommendations for wide area monitoring, protection, and control systems,” in 2012 IEEE Pow. Ener. Soc., 2012, pp. 1–6.
  • Y. Wang, M. M. Amin, J. Fu, and H. B. Moussa, “A novel data analytical approach for false data injection cyber-physical attack mitigation in smart grids,” IEEE Access, vol. 5, pp. 26 022– 26 033, 2017.
  • J. Wei and G. J. Mendis, “A deep learning-based cyber-physical strategy to mitigate false data injection attack in smart grids,” in 2016 Joint Workshop on Cyber-Physical Security and Resilience in Smart Grids (CPSR-SG). IEEE, 2016, pp. 1–6.
  • M.-J. Kang and J.-W. Kang, “Intrusion detection system using deep neural network for in-vehicle network security,” PloS one, vol. 11, no. 6, p. e0155781, 2016.
  • T. Ma, F. Wang, J. Cheng, Y. Yu, and X. Chen, “A hybrid spectral clustering and deep neural network ensemble algorithm for intrusion detection in sensor networks,” Sensors, vol. 16, no. 10, p. 1701, 2016.
  • O. Kosut, L. Jia, R. J. Thomas, and L. Tong, “Malicious data attacks on the smart grid,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 645–658, 2011.
  • L. Liu, M. Esmalifalak, Q. Ding, V. A. Emesih, and Z. Han, “Detecting false data injection attacks on power grid by sparse optimization,” IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 612– 621, 2014.
  • R. Deng, G. Xiao, R. Lu, H. Liang, and A. V. Vasilakos, “False data injection on state estimation in power systems—attacks, impacts, and defense: A survey,” IEEE Trans. Industr. Inform., vol. 13, no. 2, pp. 411–423, 2016.
  • M. Liao, D. Shi, Z. Yu, Z. Yi, Z. Wang, and Y. Xiang, “An alternating direction method of multipliers based approach for pmu data recovery,” IEEE Trans. Smart Grid, vol. 10, no. 4, pp. 4554–4565, 2018.
  • J. Zhao, L. Mili, and M. Wang, “A generalized false data injection attacks against power system nonlinear state estimator and countermeasures,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 4868–4877, 2018.
  • Mississippi State University Critical Infrastructure Protection Center, “Industrial Control System Cyber Attack Data Set.” Accessed Sept. 18, 2022. [Online]. Available: http: //www.ece.msstate.edu/wiki/index.php/ICS-Attack-Dataset
  • R. C. B. Hink, J. M. Beaver, M. A. Buckner, T. Morris, U. Adhikari, and S. Pan, “Machine learning for power system disturbance and cyber-attack discrimination,” in 2014 7th International symposium on resilient control systems (ISRCS). IEEE, 2014, pp. 1–8.
  • H. Hoffmann, “Kernel PCA for novelty detection,” Pattern Recognit., vol. 40, no. 3, pp. 863–874, 2007.
  • F. Meng, Y. Fu, and F. Lou, “A network threat analysis method combined with kernel PCA and LSTM-RNN,” in 2018 Tenth Int. Conf. Adv. Comput. Intel. (ICACI). IEEE, 2018, pp. 508– 513.
  • F. Meng, Y. Fu, F. Lou, and Z. Chen, “An effective network attack detection method based on kernel PCA and LSTMRNN,” in 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC). IEEE, 2018, pp. 568–572.
  • D. B. Rubin and R. J. Little, Statistical analysis with missing data. John Wiley & Sons, 2019.
  • T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago, and O. Tabona, “A survey on missing data in machine learning,” J. Big Data, vol. 8, no. 1, pp. 1–37, 2021.
  • M. Esmalifalak, L. Liu, N. Nguyen, R. Zheng, and Z. Han, “Detecting stealthy false data injection using machine learning in smart grid,” IEEE Syst. J., vol. 11, no. 3, pp. 1644–1652, 2014.
  • J. Sakhnini, H. Karimipour, and A. Dehghantanha, “Smart grid cyber attacks detection using supervised learning and heuristic feature selection,” in 2019 IEEE 7th international conference on smart energy grid engineering (SEGE), 2019, pp. 108–112.
  • M. Hasan, M. M. Islam, M. I. I. Zarif, and M. Hashem, “Attack and anomaly detection in iot sensors in iot sites using machine learning approaches,” IEEE Internet Things J., vol. 7, p. 100059, 2019.
  • P. K. Jena, S. Ghosh, E. Koley, and M. Manohar, “An ensemble classifier based scheme for detection of false data attacks aiming at disruption of electricity market operation,” J. Netw. Syst. Manag., vol. 29, no. 4, pp. 1–26, 2021.
  • N. Farnaaz and M. Jabbar, “Random forest modeling for network intrusion detection system,” Procedia Comput. Sci., vol. 89, pp. 213–217, 2016.
  • J. Waring, C. Lindvall, and R. Umeton, “Automated machine learning: Review of the state-of-the-art and opportunities for healthcare,” Artif. Intell. Med., vol. 104, p. 101822, 2020.
  • A. Tabakhpour and M. M. Abdelaziz, “Neural network model for false data detection in power system state estimation,” in 2019 IEEE Can. Conf. Electr. Comput. Eng. (CCECE). IEEE, 2019, pp. 1–5. S. Basumallik, R. Ma, and S. Eftekharnejad, “Packet-data anomaly detection in PMU-based state estimator using convolutional neural network,” Int. J. Electr. Power Energy Syst., vol. 107, pp. 690–702, 2019.
  • A. Sayghe, J. Zhao, and C. Konstantinou, “Evasion attacks with adversarial deep learning against power system state estimation,” in 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020, pp. 1–5.
  • C. Konstantinou and M. Maniatakos, “A data-based detection method against false data injection attacks,” IEEE Des. Test, vol. 37, no. 5, pp. 67–74, 2019.
  • M. Ozay, I. Esnaola, F. T. Y. Vural, S. R. Kulkarni, and H. V. Poor, “Machine learning methods for attack detection in the smart grid,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 8, pp. 1773–1786, 2015.
  • L. Cai, N. F. Thornhill, S. Kuenzel, and B. C. Pal, “Widearea monitoring of power systems using principal component analysis and k-nearest neighbor analysis,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 4913–4923, 2018.
  • S. Manocha, V. Bansal, I. Kaushal, and A. Bhat, “Efficient power theft detection using smart meter data in advanced metering infrastructure,” in 2020 4th Int. Conf. Intell. Comput. Control Syst. (ICICCS). IEEE, 2020, pp. 765–770.
  • B. C. Costa, B. L. Alberto, A. M. Portela, W. Maduro, and E. O. Eler, “Fraud detection in electric power distribution networks using an ann-based knowledge-discovery process,” Int. J. Artif. Intell., vol. 4, no. 6, p. 17, 2013.
  • M. N. Hasan, R. N. Toma, A.-A. Nahid, M. M. Islam, and J.-M. Kim, “Electricity theft detection in smart grid systems: A CNN-LSTM based approach,” Energies, vol. 12, no. 17, p. 3310, 2019.
There are 50 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Kübra Bitirgen 0000-0002-4468-4905

Ümmühan Başaran Filik 0000-0002-0715-821X

Publication Date December 31, 2022
Submission Date September 13, 2022
Published in Issue Year 2022 Volume: 11 Issue: 4

Cite

IEEE K. Bitirgen and Ü. Başaran Filik, “Performance Analysis of PCA Based Machine Learning Approaches on FDIA Detection”, IJISS, vol. 11, no. 4, pp. 1–13, 2022.