Deteksi Serangan Denial of Service pada Internet of Things Menggunakan Finite-State Automata
Keywords:
detection, DoS, finite-state automata, IoT, prevention
Abstract
Internet of things memiliki kemampuan untuk menghubungkan obyek pintar dan memungkinkan mereka untuk berinteraksi dengan lingkungan dan peralatan komputasi cerdas lainnya melalui jaringan internet. Namun belakangan ini, keamanan jaringan internet of things mendapat ancaman akibat serangan cyber yang dapat menembus perangkat internet of things target dengan menggunakan berbagai serangan denial of service. Penelitian ini bertujuan untuk mendeteksi dan mencegah serangan denial of service berupa synchronize flooding dan ping flooding pada jaringan internet of things dengan pendekatan finite-state automata. Hasil pengujian menunjukkan bahwa pendekatan finite-state automata berhasil mendeteksi serangan synchronize flooding dan ping flooding pada jaringan internet of things, tetapi pencegahan serangan tidak secara signifikan mengurangi penggunaan prosesor dan memori. Serangan synchronize flooding menyebabkan delay saat mengaktifkan/menonaktifkan peralatan internet of things sedangkan serangan ping flooding menyebabkan error. Implementasi bash-iptables berhasil mengurangi serangan synchronize flooding dengan efisiensi waktu pencegahan sebesar 55,37% dan mengurangi serangan ping flooding sebesar 60% tetapi dengan waktu yang tidak signifikan.
Downloads
Download data is not yet available.
References
[1] R. Paudel, T. Muncy, and W. Eberle, “Detecting DoS Attack in Smart Home IoT Devices Using a Graph-Based Approach,” in 2019 IEEE International Conference on Big Data (Big Data), pp. 5249–5258, Dec. 9-12, 2019.
[2] Y. Al-Hadhrami and F. K. Hussain, “A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT,” Advances in Intelligent Systems and Computing, Switzerland: Springer, 2020, 417–429.
[3] A. Bamou, M. Khardioui, M. D. El Ouadghiri, and B. Aghoutane, “Implementing and Evaluating an Intrusion Detection System for Denial of Service Attacks in IoT Environments,” Lecture Notes in Networks and Systems, Switzerland: Springer, 2020, 167–178.
[4] G. R. Andreica, L. Bozga, D. Zinca, and V. Dobrota, “Denial of Service and Man-in-the-Middle Attacks Against IoT Devices in a GPS-Based Monitoring Software for Intelligent Transportation Systems,” in 19th RoEduNet Conference: Networking in Education and Research (RoEduNet), pp. 1–4, Dec. 11-12, 2020.
[5] R. Ande, B. Adebisi, M. Hammoudeh, and J. Saleem, “Internet of Things: Evolution and technologies from a security perspective,” Sustain. Cities Soc., vol. 54, pp. 1–45, March 2020.
[6] S. S. Kumar and K. Kulothungan, “An Anomaly Behavior based Detection and Prevention of DoS Attack in IoT Environment,” in Ninth International Conference on Advanced Computing (ICoAC), pp. 287–292, Dec. 14-16, 2017.
[7] W. Ashford, “Businesses blame rivals for DDoS attacks,” Computer Weekly, 3 March 2017, [Online]. Tersedia: https://www.computerweekly.com/news/450414239/Businesses-blame-rivals-for-DDoS-attacks.
[8] L. Liang, K. Zheng, Q. Sheng, and X. Huang, “A Denial of Service Attack Method for an IoT System,” in 8th International Conference on Information Technology in Medicine and Education (ITME), pp. 360–364, Dec. 23-25, 2016.
[9] A. Sanmorino and R. Gustriansyah, “An alternative solution to handle ddos attacks,” J. Theor. Appl. Inf. Technol., vol. 96, no. 3, pp. 657–667, February 2018.
[10] G. B. Gunawan, P. Sukarno, and A. G. Putrada, “Pendeteksian Serangan Denial of Service (DoS) pada Perangkat Smartlock Berbasis Wifi Menggunakan SNORT IDS,” eProceedings Eng., vol. 5, no. 3, pp. 7875–7884, December 2018.
[11] I. Walad, 2020, Analisis Denial Of Service Attack Pada Sistem Keamanan Web. Tesis. Medan: Universitas Sumatera Utara.
[12] M. Ge, N. F. Syed, X. Fu, Z. Baig, and A. Robles-Kelly, “Towards a deep learning-driven intrusion detection approach for Internet of Things,” Comput. Networks, vol. 186, p. 107784, February 2021.
[13] J. Gao et al., “Omni SCADA Intrusion Detection Using Deep Learning Algorithms,” IEEE Internet Things J., vol. 8, no. 2, pp. 951–961, January 2021.
[14] T.-H. Lee, L.-H. Chang, and C.-W. Syu, “Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks,” in IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6, Jun. 7, 2020.
[15] P. Nagar, H. K. Menaria, and M. Tiwari, “Novel Approach of Intrusion Detection Classification Deeplearning Using SVM,” Advances in Intelligent Systems and Computing, Switzerland: Springer, 2020, 365–381.
[16] B. Susilo and R. F. Sari, “Intrusion Detection in IoT Networks Using Deep Learning Algorithm,” Information, vol. 11, no. 5, pp. 1–11, May 2020.
[17] N. F. Syed, Z. Baig, A. Ibrahim, and C. Valli, “Denial of service attack detection through machine learning for the IoT,” J. Inf. Telecommun., vol. 4, no. 4, pp. 482–503, October 2020.
[18] K. Pradeep Mohan Kumar, M. Saravanan, M. Thenmozhi, and K. Vijayakumar, “Intrusion detection system based on GA‐fuzzy classifier for detecting malicious attacks,” Concurr. Comput. Pract. Exp., vol. 33, no. 3, February 2021.
[19] S. Ramesh, C. Yaashuwanth, K. Prathibanandhi, A. R. Basha, and T. Jayasankar, “An optimized deep neural network based DoS attack detection in wireless video sensor network,” J. Ambient Intell. Humaniz. Comput., January 2021.
[20] S. Latif, Z. Zou, Z. Idrees, and J. Ahmad, “A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network,” IEEE Access, vol. 8, pp. 89337–89350, May 2020.
[21] M. Eskandari, Z. H. Janjua, M. Vecchio, and F. Antonelli, “Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices,” IEEE Internet Things J., vol. 7, no. 8, pp. 6882–6897, August 2020.
[22] J. Pacheco, V. H. Benitez, L. C. Felix-Herran, and P. Satam, “Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes,” IEEE Access, vol. 8, pp. 73907–73918, March 2020.
[23] K. Zhang, L. Zhang, and L. Xie, “Detectability of Finite-State Automata,” in Communications and Control Engineering, Switzerland: Springer, Cham, 2020, 179–192.
[24] A. A. K. O. Sudana, W. P. Buana, and T. Wulandari, “Web-based Implementation of Finite State Automata Method on Lyrics Recognition System of Balinese Song ‘Pupuh,’” Int. J. Comput. Appl., vol. 149, no. 4, pp. 32–37, September 2016.
[25] Y. Akbari, K. Nouri, J. Sadri, C. Djeddi, and I. Siddiqi, “Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata,” Image Vis. Comput., vol. 59, pp. 17–30, March 2017.
[26] Y. M. R. Putra, “Sentence Analysis With Artificial Intelligence Machine Learning Using Finite State Automata,” Proxies, vol. 1, no. 1, pp. 1–6, July 2017.
[27] B. Fernandus, 2016, Separating of the Words using Finite State Automata. Tesis. Semarang: Unika Soegijapranata.
[28] M. AbuSafiya, “Measuring Documents Similarity using Finite State Automata,” in 2020 2nd International Conference on Mathematics and Information Technology (ICMIT), pp. 208–211, Feb. 18-19, 2020.
[29] C. Kara-Mohamed, A. Hamdi-Cherif, H. Al’Alwi, K. Al-Khalifa, and N. Al-Harbi, “Grammatical Inference System for Finite State Automata - GIFSA,” in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1274–1279, Dec. 15-17, 2016.
[30] J. M. R. Imperial, C. G. V. Ya-On, and J. C. Ureta, “An experimental Tagalog Finite State Automata spellchecker with Levenshtein edit-distance feature,” in 2019 International Conference on Asian Language Processing (IALP), pp. 240–243, Nov. 15-17, 2019.
[31] I. B. T. T. T. T. Murti, C. Janis, and I. G. A. A. Sudhana, “Transliteration Balinese Script using Finite State Automata (FSA) Algorithm,” J. Phys. Conf. Ser., vol. 1165, no. 1, p. 012002, February 2019.
[32] P. N. Crisnapati, P. D. Novayanti, G. Indrawan, K. Y. E. Aryanto, and M. S. Wibawa, “Accuracy Analysis of Pasang Aksara Bot using Finite State Automata Transliteration Method,” in 2018 6th International Conference on Cyber and IT Service Management (CITSM), pp. 1–6, Aug. 7-9, 2018.
[33] A. Swetapadma and A. Yadav, “An innovative finite state automata based approach for fault direction estimation in transmission lines,” Measurement, vol. 99, pp. 13–22, March 2017.
[34] T. Peng, L. Dai, Z. Chen, C. Ye, and X. Peng, “A Probabilistic Finite State Automata-based Fault Detection Method for Traction Motor,” in 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), pp. 1199–1204, Jun. 17-19, 2020.
[35] F. Settele, A. Weber, and A. Knoll, “Plant Model-Based Fault Detection during Aircraft Takeoff Using Non-Deterministic Finite-State Automata,” Aerospace, vol. 7, no. 8, pp. 1–15, July 2020.
[36] C. Bhattacharya, S. Dharmadhikari, A. Basak, and A. Ray, “Early Detection of Fatigue Crack Damage in Ductile Materials: A Projection-Based Probabilistic Finite State Automata Approach,” ASME Lett. Dyn. Syst. Control, vol. 1, no. 4, October 2021.
[37] T. Y. Pribadi, K. Handayani, and W. Gata, “Diagnosis of Heart Disease Using Automata Finite State Algorithm,” Techno Nusa Mandiri, vol. 18, no. 1, pp. 17–24, March 2021.
[38] F. J. Kaunang and J. Waworundeng, “Implementation of Finite State Automata in an Amusement Park Automatic Ticket Selling Machine,” Abstr. Proc. Int. Sch. Conf., vol. 7, no. 1, pp. 1776–1785, December 2019.
[39] K. Handayani, D. Ismunandar, S. A. Putri, and W. Gata, “Penerapan Finite State Automata pada Vending Machine Susu Kambing Etawa,” MATICS, vol. 12, no. 2, pp. 87–92, March 2021.
[40] E. Erni, F. Titiani, S. A. Putri, and W. Gata, “Penerapan Konsep Finite State Automata Pada Aplikasi Simulasi Vending Machine Jamu Tradisional,” J. Inform., vol. 7, no. 2, pp. 141–147, September 2020.
[41] L. Sauer, D. Henrich, and W. Martens, “Towards Intuitive Robot Programming Using Finite State Automata,” Lecture Notes in Computer Science, Switzerland: Springer, 2019, 290–298.
[42] M. Jaluvka, E. Volna, and M. Kotyrba, “Motion Controlling Using Finite-State Automata,” Intelligent Information and Database Systems, 2019, 455–464.
[43] R. Karmakar, S. S. Jana, and S. Chattopadhyay, “A Cellular Automata Guided Finite-State-Machine Watermarking Strategy for IP Protection of Sequential Circuits,” IEEE Trans. Emerg. Top. Comput., pp. 1–1, December 2020.
[44] R. O. Reddy, K. D. Dhruve, R. N. Reddy, M. Radha, and N. S. Vani, “A Novel Approach in Adopting Finite State Automata for Image Processing Applications,” Int. J. Comput. Vis. Image Process., vol. 8, no. 1, pp. 59–74, January 2018.
[45] S. A. Valianta, “Identifikasi Serangan Port Scanning dengan Metode String Matching,” Annu. Res. Semin., vol. 2, no. 1, pp. 466–471, May 2016.
[46] Q. Chen, H. Chen, Y. Cai, Y. Zhang, and X. Huang, “Denial of Service Attack on IoT System,” in 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 755–758, Oct. 19-21, 2018.
[47] Y. Cui, Q. Liu, K. Zheng, and X. Huang, “Evaluation of Several Denial of Service Attack Methods for IoT System,” in 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 794–798, Oct. 19-21, 2018.
[2] Y. Al-Hadhrami and F. K. Hussain, “A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT,” Advances in Intelligent Systems and Computing, Switzerland: Springer, 2020, 417–429.
[3] A. Bamou, M. Khardioui, M. D. El Ouadghiri, and B. Aghoutane, “Implementing and Evaluating an Intrusion Detection System for Denial of Service Attacks in IoT Environments,” Lecture Notes in Networks and Systems, Switzerland: Springer, 2020, 167–178.
[4] G. R. Andreica, L. Bozga, D. Zinca, and V. Dobrota, “Denial of Service and Man-in-the-Middle Attacks Against IoT Devices in a GPS-Based Monitoring Software for Intelligent Transportation Systems,” in 19th RoEduNet Conference: Networking in Education and Research (RoEduNet), pp. 1–4, Dec. 11-12, 2020.
[5] R. Ande, B. Adebisi, M. Hammoudeh, and J. Saleem, “Internet of Things: Evolution and technologies from a security perspective,” Sustain. Cities Soc., vol. 54, pp. 1–45, March 2020.
[6] S. S. Kumar and K. Kulothungan, “An Anomaly Behavior based Detection and Prevention of DoS Attack in IoT Environment,” in Ninth International Conference on Advanced Computing (ICoAC), pp. 287–292, Dec. 14-16, 2017.
[7] W. Ashford, “Businesses blame rivals for DDoS attacks,” Computer Weekly, 3 March 2017, [Online]. Tersedia: https://www.computerweekly.com/news/450414239/Businesses-blame-rivals-for-DDoS-attacks.
[8] L. Liang, K. Zheng, Q. Sheng, and X. Huang, “A Denial of Service Attack Method for an IoT System,” in 8th International Conference on Information Technology in Medicine and Education (ITME), pp. 360–364, Dec. 23-25, 2016.
[9] A. Sanmorino and R. Gustriansyah, “An alternative solution to handle ddos attacks,” J. Theor. Appl. Inf. Technol., vol. 96, no. 3, pp. 657–667, February 2018.
[10] G. B. Gunawan, P. Sukarno, and A. G. Putrada, “Pendeteksian Serangan Denial of Service (DoS) pada Perangkat Smartlock Berbasis Wifi Menggunakan SNORT IDS,” eProceedings Eng., vol. 5, no. 3, pp. 7875–7884, December 2018.
[11] I. Walad, 2020, Analisis Denial Of Service Attack Pada Sistem Keamanan Web. Tesis. Medan: Universitas Sumatera Utara.
[12] M. Ge, N. F. Syed, X. Fu, Z. Baig, and A. Robles-Kelly, “Towards a deep learning-driven intrusion detection approach for Internet of Things,” Comput. Networks, vol. 186, p. 107784, February 2021.
[13] J. Gao et al., “Omni SCADA Intrusion Detection Using Deep Learning Algorithms,” IEEE Internet Things J., vol. 8, no. 2, pp. 951–961, January 2021.
[14] T.-H. Lee, L.-H. Chang, and C.-W. Syu, “Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks,” in IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6, Jun. 7, 2020.
[15] P. Nagar, H. K. Menaria, and M. Tiwari, “Novel Approach of Intrusion Detection Classification Deeplearning Using SVM,” Advances in Intelligent Systems and Computing, Switzerland: Springer, 2020, 365–381.
[16] B. Susilo and R. F. Sari, “Intrusion Detection in IoT Networks Using Deep Learning Algorithm,” Information, vol. 11, no. 5, pp. 1–11, May 2020.
[17] N. F. Syed, Z. Baig, A. Ibrahim, and C. Valli, “Denial of service attack detection through machine learning for the IoT,” J. Inf. Telecommun., vol. 4, no. 4, pp. 482–503, October 2020.
[18] K. Pradeep Mohan Kumar, M. Saravanan, M. Thenmozhi, and K. Vijayakumar, “Intrusion detection system based on GA‐fuzzy classifier for detecting malicious attacks,” Concurr. Comput. Pract. Exp., vol. 33, no. 3, February 2021.
[19] S. Ramesh, C. Yaashuwanth, K. Prathibanandhi, A. R. Basha, and T. Jayasankar, “An optimized deep neural network based DoS attack detection in wireless video sensor network,” J. Ambient Intell. Humaniz. Comput., January 2021.
[20] S. Latif, Z. Zou, Z. Idrees, and J. Ahmad, “A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network,” IEEE Access, vol. 8, pp. 89337–89350, May 2020.
[21] M. Eskandari, Z. H. Janjua, M. Vecchio, and F. Antonelli, “Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices,” IEEE Internet Things J., vol. 7, no. 8, pp. 6882–6897, August 2020.
[22] J. Pacheco, V. H. Benitez, L. C. Felix-Herran, and P. Satam, “Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes,” IEEE Access, vol. 8, pp. 73907–73918, March 2020.
[23] K. Zhang, L. Zhang, and L. Xie, “Detectability of Finite-State Automata,” in Communications and Control Engineering, Switzerland: Springer, Cham, 2020, 179–192.
[24] A. A. K. O. Sudana, W. P. Buana, and T. Wulandari, “Web-based Implementation of Finite State Automata Method on Lyrics Recognition System of Balinese Song ‘Pupuh,’” Int. J. Comput. Appl., vol. 149, no. 4, pp. 32–37, September 2016.
[25] Y. Akbari, K. Nouri, J. Sadri, C. Djeddi, and I. Siddiqi, “Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata,” Image Vis. Comput., vol. 59, pp. 17–30, March 2017.
[26] Y. M. R. Putra, “Sentence Analysis With Artificial Intelligence Machine Learning Using Finite State Automata,” Proxies, vol. 1, no. 1, pp. 1–6, July 2017.
[27] B. Fernandus, 2016, Separating of the Words using Finite State Automata. Tesis. Semarang: Unika Soegijapranata.
[28] M. AbuSafiya, “Measuring Documents Similarity using Finite State Automata,” in 2020 2nd International Conference on Mathematics and Information Technology (ICMIT), pp. 208–211, Feb. 18-19, 2020.
[29] C. Kara-Mohamed, A. Hamdi-Cherif, H. Al’Alwi, K. Al-Khalifa, and N. Al-Harbi, “Grammatical Inference System for Finite State Automata - GIFSA,” in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1274–1279, Dec. 15-17, 2016.
[30] J. M. R. Imperial, C. G. V. Ya-On, and J. C. Ureta, “An experimental Tagalog Finite State Automata spellchecker with Levenshtein edit-distance feature,” in 2019 International Conference on Asian Language Processing (IALP), pp. 240–243, Nov. 15-17, 2019.
[31] I. B. T. T. T. T. Murti, C. Janis, and I. G. A. A. Sudhana, “Transliteration Balinese Script using Finite State Automata (FSA) Algorithm,” J. Phys. Conf. Ser., vol. 1165, no. 1, p. 012002, February 2019.
[32] P. N. Crisnapati, P. D. Novayanti, G. Indrawan, K. Y. E. Aryanto, and M. S. Wibawa, “Accuracy Analysis of Pasang Aksara Bot using Finite State Automata Transliteration Method,” in 2018 6th International Conference on Cyber and IT Service Management (CITSM), pp. 1–6, Aug. 7-9, 2018.
[33] A. Swetapadma and A. Yadav, “An innovative finite state automata based approach for fault direction estimation in transmission lines,” Measurement, vol. 99, pp. 13–22, March 2017.
[34] T. Peng, L. Dai, Z. Chen, C. Ye, and X. Peng, “A Probabilistic Finite State Automata-based Fault Detection Method for Traction Motor,” in 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), pp. 1199–1204, Jun. 17-19, 2020.
[35] F. Settele, A. Weber, and A. Knoll, “Plant Model-Based Fault Detection during Aircraft Takeoff Using Non-Deterministic Finite-State Automata,” Aerospace, vol. 7, no. 8, pp. 1–15, July 2020.
[36] C. Bhattacharya, S. Dharmadhikari, A. Basak, and A. Ray, “Early Detection of Fatigue Crack Damage in Ductile Materials: A Projection-Based Probabilistic Finite State Automata Approach,” ASME Lett. Dyn. Syst. Control, vol. 1, no. 4, October 2021.
[37] T. Y. Pribadi, K. Handayani, and W. Gata, “Diagnosis of Heart Disease Using Automata Finite State Algorithm,” Techno Nusa Mandiri, vol. 18, no. 1, pp. 17–24, March 2021.
[38] F. J. Kaunang and J. Waworundeng, “Implementation of Finite State Automata in an Amusement Park Automatic Ticket Selling Machine,” Abstr. Proc. Int. Sch. Conf., vol. 7, no. 1, pp. 1776–1785, December 2019.
[39] K. Handayani, D. Ismunandar, S. A. Putri, and W. Gata, “Penerapan Finite State Automata pada Vending Machine Susu Kambing Etawa,” MATICS, vol. 12, no. 2, pp. 87–92, March 2021.
[40] E. Erni, F. Titiani, S. A. Putri, and W. Gata, “Penerapan Konsep Finite State Automata Pada Aplikasi Simulasi Vending Machine Jamu Tradisional,” J. Inform., vol. 7, no. 2, pp. 141–147, September 2020.
[41] L. Sauer, D. Henrich, and W. Martens, “Towards Intuitive Robot Programming Using Finite State Automata,” Lecture Notes in Computer Science, Switzerland: Springer, 2019, 290–298.
[42] M. Jaluvka, E. Volna, and M. Kotyrba, “Motion Controlling Using Finite-State Automata,” Intelligent Information and Database Systems, 2019, 455–464.
[43] R. Karmakar, S. S. Jana, and S. Chattopadhyay, “A Cellular Automata Guided Finite-State-Machine Watermarking Strategy for IP Protection of Sequential Circuits,” IEEE Trans. Emerg. Top. Comput., pp. 1–1, December 2020.
[44] R. O. Reddy, K. D. Dhruve, R. N. Reddy, M. Radha, and N. S. Vani, “A Novel Approach in Adopting Finite State Automata for Image Processing Applications,” Int. J. Comput. Vis. Image Process., vol. 8, no. 1, pp. 59–74, January 2018.
[45] S. A. Valianta, “Identifikasi Serangan Port Scanning dengan Metode String Matching,” Annu. Res. Semin., vol. 2, no. 1, pp. 466–471, May 2016.
[46] Q. Chen, H. Chen, Y. Cai, Y. Zhang, and X. Huang, “Denial of Service Attack on IoT System,” in 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 755–758, Oct. 19-21, 2018.
[47] Y. Cui, Q. Liu, K. Zheng, and X. Huang, “Evaluation of Several Denial of Service Attack Methods for IoT System,” in 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 794–798, Oct. 19-21, 2018.
Published
2021-11-26
How to Cite
Antony, F., & Gustriansyah, R. (2021). Deteksi Serangan Denial of Service pada Internet of Things Menggunakan Finite-State Automata. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(1), 43-52. https://doi.org/https://doi.org/10.30812/matrik.v21i1.1078
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png)
