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Fog Computing Based Signature Verification: A Scenario-Based Approach

Year 2019, Volume: 11 Issue: 1, 64 - 76, 31.01.2019
https://doi.org/10.29137/umagd.481464

Abstract

Today, the development of technology greatly facilitates our lives in all areas. However, transactions over the internet bring about the security threat. For this reason, controls and studies are carried out to prevent unauthorized access to personal data. One of the most important of these controls is the signature information received from the users. However, since the signature information can be simulated and played, the visual control is insufficient. For this reason, signature-specific characteristic information is the most accurate approach to be signed out, recorded and compared with subsequent signatures. Such transactions have been made and developed on cloud computing. Since all data is sent and shared over the Internet in traditional cloud computing architecture, it has disadvantages such as bandwidth, energy consumption and security. Therefore, fog information architecture has been improved and the deficiencies in traditional cloud computing have been largely eliminated. In this study, fuzz computing-based signature verification approach has been developed. A scenario has been developed and evaluated in this scenario for banks from institutions where security is handled intensively. As a result of the study; a more secure signature verification framework has been developed compared to traditional cloud computing. The study will lead the studies to be carried out.

References

  • Aazam, M., & Huh, E.-N. (2016). Fog Computing: The Cloud-IoT\/IoE Middleware Paradigm. IEEE Potentials, 35(3), 40-44. doi:10.1109/mpot.2015.2456213
  • Aazam, M., Zeadally, S., & Harras, K. A. (2018a). Fog Computing Architecture, Evaluation, and Future Research Directions. IEEE Communications Magazine, 56(5), 46-52. doi:10.1109/mcom.2018.1700707
  • Aazam, M., Zeadally, S., & Harras, K. A. (2018b). Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Future Generation Computer Systems, 87, 278-289. doi:10.1016/j.future.2018.04.057
  • Arkian, H. R., Diyanat, A., & Pourkhalili, A. (2017). MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications. Journal of Network and Computer Applications, 82, 152-165. doi:10.1016/j.jnca.2017.01.012
  • Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., . . . Rabkin, A. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. doi:10.1145/1721654.1721672
  • Connor, P., & Ross, A. (2018). Biometric recognition by gait: A survey of modalities and features. Computer Vision and Image Understanding, 167, 1-27. doi:10.1016/j.cviu.2018.01.007
  • Datta, S. K., Bonnet, C., & Haerri, J. (2015). Fog Computing architecture to enable consumer centric Internet of Things services. 1-2. doi:10.1109/isce.2015.7177778
  • Díaz, M., Martín, C., & Rubio, B. (2016). State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. Journal of Network and Computer Applications, 67, 99-117. doi:10.1016/j.jnca.2016.01.010
  • Doroz, R., Kudlacik, P., & Porwik, P. (2018). Online signature verification modeled by stability oriented reference signatures. Information Sciences, 460-461, 151-171. doi:10.1016/j.ins.2018.05.049
  • Efford, N. (2000). Digital Image Processing: A Practical Introduction using Java (1 edition ed.): Pearson.Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future Generation Computer Systems, 29(1), 84-106. doi:10.1016/j.future.2012.05.023
  • Ghazouani, S., & Slimani, Y. (2017). A survey on cloud service description. Journal of Network and Computer Applications, 91, 61-74. doi:10.1016/j.jnca.2017.04.013
  • Hajibaba, M., & Gorgin, S. (2014). A Review on Modern Distributed Computing Paradigms: Cloud Computing, Jungle Computing and Fog Computing. Journal of Computing and Information Technology, 22(2), 69. doi:10.2498/cit.1002381
  • Ito, T., Ohyama, W., Wakabayashi, T., & Kimura, F. (2012). Combination of Signature Verification Techniques by SVM. 430-433. doi:10.1109/icfhr.2012.192
  • Kawazoe, Y., Ohyama, W., Wakabayashi, T., & Kimura, F. (2010). Improvement of On-line Signature Verification Based on Gradient Features. 410-414. doi:10.1109/icfhr.2010.70
  • Kekre, H. B., & Bharadi, V. A. (2011). Dynamic signature pre-processing by modified digital difference analyzer algorithm, New Delhi.
  • Kekre, H. B., Bharadi, V. A., & Sarode, T. K. (2011). Dynamic signature using time based vector quantization by Kekre’s median codebook generation algorithm, New Delhi.
  • Mahmud, R., Srirama, S. N., Ramamohanarao, K., & Buyya, R. (2018). Quality of Experience (QoE)-aware placement of applications in Fog computing environments. Journal of Parallel and Distributed Computing. doi:10.1016/j.jpdc.2018.03.004
  • Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. U.S. Department of Commerce Computer Security Division Information Technology Laboratory: NIST.
  • Min, C., Yixue, H., Yong, L., Chin-Feng, L., & Di, W. (2015). On the computation offloading at ad hoc cloudlet: architecture and service modes. IEEE Communications Magazine, 53(6), 18-24. doi:10.1109/mcom.2015.7120041
  • Muramatsu, D., & Yagi, Y. (2013). Silhouette-based online signature verification using pen tip trajectory and pen holding style. 1-8. doi:10.1109/icb.2013.6612958
  • Nobre, J. C., de Souza, A. M., Rosário, D., Both, C., Villas, L. A., Cerqueira, E., . . . Gerla, M. (2019). Vehicular Software-Defined Networking and fog computing: Integration and design principles. Ad Hoc Networks, 82, 172-181. doi:10.1016/j.adhoc.2018.07.016
  • Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66. doi:10.1109/tsmc.1979.4310076
  • Qiu, T., Zheng, K., Song, H., Han, M., & Kantarci, B. (2017). A Local-Optimization Emergency Scheduling Scheme With Self-Recovery for a Smart Grid. IEEE Transactions on Industrial Informatics, 13(6), 3195-3205. doi:10.1109/tii.2017.2715844
  • Radhika, K. S., & Gopika, S. (2015). Online and Offline Signature Verification: A Combined Approach. Procedia Computer Science, 46, 1593-1600. doi:10.1016/j.procs.2015.02.089
  • Salman, O., Elhajj, I., Chehab, A., & Kayssi, A. (2018). IoT survey: An SDN and fog computing perspective. Computer Networks, 143, 221-246. doi:10.1016/j.comnet.2018.07.020
  • Singh, A., & Chatterjee, K. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79, 88-115. doi:10.1016/j.jnca.2016.11.027
  • Stojmenovic, I., & Wen, S. (2014). The Fog Computing Paradigm: Scenarios and Security Issues. 2, 1-8. doi:10.15439/2014f503
  • Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., & Yang, Q. (2017). Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities. IEEE Transactions on Industrial Informatics, 13(5), 2140-2150. doi:10.1109/tii.2017.2679740
  • Yannuzzi, M., Milito, R., Serral-Gracia, R., Montero, D., & Nemirovsky, M. (2014). Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing. 325-329. doi:10.1109/camad.2014.7033259
  • Yi, S., Hao, Z., Qin, Z., & Li, Q. (2015). Fog Computing: Platform and Applications. 73-78. doi:10.1109/HotWeb.2015.22
  • Yu, C., Songqing, C., Peng, H., & Brown, D. (2015). FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation. 2-11. doi:10.1109/nas.2015.7255196
  • Zhang, H., Xiao, Y., Bu, S., Niyato, D., Yu, R., & Han, Z. (2016). Fog computing in multi-tier data center networks: A hierarchical game approach. 1-6. doi:10.1109/icc.2016.7511146
  • Zhang, Y., Niyato, D., & Wang, P. (2015). Offloading in Mobile Cloudlet Systems with Intermittent Connectivity. IEEE Transactions on Mobile Computing, 14(12), 2516-2529. doi:10.1109/tmc.2015.2405539
  • Zhou, W., & Zhang, D. (1999). Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 46(1), 78-80. doi:10.1109/82.749102

Sis Bilişim Tabanlı İmza Doğrulama: Senaryoya Dayalı Bir Yaklaşım

Year 2019, Volume: 11 Issue: 1, 64 - 76, 31.01.2019
https://doi.org/10.29137/umagd.481464

Abstract

Günümüzde teknolojinin gelişmesi hayatımızı her alanda büyük oranda kolaylaştırmaktadır. Ancak internet üzerinden yapılan işlemler güvenlik tehdidini beraberinde getirmektedir. Bu nedenle kişiye özel bir veriye yetkisiz kişiler tarafından erişimin engellenmesi için kontroller ve çalışmalar yapılmaktadır. Bu kontrollerden en önemlilerinden biri kullanıcılardan alınan imza bilgisidir. Ancak imza bilgisi taklit edilebilir ve çalınabilir olduğundan gözle kontrolü yetersiz kalmaktadır. Bu nedenle imzaya özgü karakteristik bilgilerin imzadan çıkarılması, kaydedilmesi ve sonraki imzalarla karşılaştırılması en doğru yaklaşımdır. Bu gibi işlemler bulut bilişim üzerinde yapılmış ve geliştirilmiştir. Geleneksel bulut bilişim mimarisinde tüm veriler internet üzerinden gönderildiği ve paylaşıldığından güvenlik başta olmak üzere bant genişliği, enerji sarfiyatı gibi dezavantajlar barındırmaktadır. Bu nedenle sis bilişim mimarisi geliştirilmiş ve geleneksel bulut bilişimde yer alan eksiklikler büyük oranda giderilmiştir. Bu çalışmada sis bilişim tabanlı imza doğrulama yaklaşımı geliştirilmiştir. Güvenliğin yoğun olarak ele alındığı kurumlardan olan bankalar için bir senaryo geliştirilmiş ve bu senaryo ile değerlendirilmiştir. Çalışmanın sonucunda; geleneksel bulut bilişime kıyasla daha güvenli bir imza doğrulama çerçevesi ortaya konulmuştur. Yapılan çalışma bundan sonra yapılacak çalışmalara öncülük edecektir.

References

  • Aazam, M., & Huh, E.-N. (2016). Fog Computing: The Cloud-IoT\/IoE Middleware Paradigm. IEEE Potentials, 35(3), 40-44. doi:10.1109/mpot.2015.2456213
  • Aazam, M., Zeadally, S., & Harras, K. A. (2018a). Fog Computing Architecture, Evaluation, and Future Research Directions. IEEE Communications Magazine, 56(5), 46-52. doi:10.1109/mcom.2018.1700707
  • Aazam, M., Zeadally, S., & Harras, K. A. (2018b). Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Future Generation Computer Systems, 87, 278-289. doi:10.1016/j.future.2018.04.057
  • Arkian, H. R., Diyanat, A., & Pourkhalili, A. (2017). MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications. Journal of Network and Computer Applications, 82, 152-165. doi:10.1016/j.jnca.2017.01.012
  • Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., . . . Rabkin, A. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. doi:10.1145/1721654.1721672
  • Connor, P., & Ross, A. (2018). Biometric recognition by gait: A survey of modalities and features. Computer Vision and Image Understanding, 167, 1-27. doi:10.1016/j.cviu.2018.01.007
  • Datta, S. K., Bonnet, C., & Haerri, J. (2015). Fog Computing architecture to enable consumer centric Internet of Things services. 1-2. doi:10.1109/isce.2015.7177778
  • Díaz, M., Martín, C., & Rubio, B. (2016). State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. Journal of Network and Computer Applications, 67, 99-117. doi:10.1016/j.jnca.2016.01.010
  • Doroz, R., Kudlacik, P., & Porwik, P. (2018). Online signature verification modeled by stability oriented reference signatures. Information Sciences, 460-461, 151-171. doi:10.1016/j.ins.2018.05.049
  • Efford, N. (2000). Digital Image Processing: A Practical Introduction using Java (1 edition ed.): Pearson.Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future Generation Computer Systems, 29(1), 84-106. doi:10.1016/j.future.2012.05.023
  • Ghazouani, S., & Slimani, Y. (2017). A survey on cloud service description. Journal of Network and Computer Applications, 91, 61-74. doi:10.1016/j.jnca.2017.04.013
  • Hajibaba, M., & Gorgin, S. (2014). A Review on Modern Distributed Computing Paradigms: Cloud Computing, Jungle Computing and Fog Computing. Journal of Computing and Information Technology, 22(2), 69. doi:10.2498/cit.1002381
  • Ito, T., Ohyama, W., Wakabayashi, T., & Kimura, F. (2012). Combination of Signature Verification Techniques by SVM. 430-433. doi:10.1109/icfhr.2012.192
  • Kawazoe, Y., Ohyama, W., Wakabayashi, T., & Kimura, F. (2010). Improvement of On-line Signature Verification Based on Gradient Features. 410-414. doi:10.1109/icfhr.2010.70
  • Kekre, H. B., & Bharadi, V. A. (2011). Dynamic signature pre-processing by modified digital difference analyzer algorithm, New Delhi.
  • Kekre, H. B., Bharadi, V. A., & Sarode, T. K. (2011). Dynamic signature using time based vector quantization by Kekre’s median codebook generation algorithm, New Delhi.
  • Mahmud, R., Srirama, S. N., Ramamohanarao, K., & Buyya, R. (2018). Quality of Experience (QoE)-aware placement of applications in Fog computing environments. Journal of Parallel and Distributed Computing. doi:10.1016/j.jpdc.2018.03.004
  • Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. U.S. Department of Commerce Computer Security Division Information Technology Laboratory: NIST.
  • Min, C., Yixue, H., Yong, L., Chin-Feng, L., & Di, W. (2015). On the computation offloading at ad hoc cloudlet: architecture and service modes. IEEE Communications Magazine, 53(6), 18-24. doi:10.1109/mcom.2015.7120041
  • Muramatsu, D., & Yagi, Y. (2013). Silhouette-based online signature verification using pen tip trajectory and pen holding style. 1-8. doi:10.1109/icb.2013.6612958
  • Nobre, J. C., de Souza, A. M., Rosário, D., Both, C., Villas, L. A., Cerqueira, E., . . . Gerla, M. (2019). Vehicular Software-Defined Networking and fog computing: Integration and design principles. Ad Hoc Networks, 82, 172-181. doi:10.1016/j.adhoc.2018.07.016
  • Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66. doi:10.1109/tsmc.1979.4310076
  • Qiu, T., Zheng, K., Song, H., Han, M., & Kantarci, B. (2017). A Local-Optimization Emergency Scheduling Scheme With Self-Recovery for a Smart Grid. IEEE Transactions on Industrial Informatics, 13(6), 3195-3205. doi:10.1109/tii.2017.2715844
  • Radhika, K. S., & Gopika, S. (2015). Online and Offline Signature Verification: A Combined Approach. Procedia Computer Science, 46, 1593-1600. doi:10.1016/j.procs.2015.02.089
  • Salman, O., Elhajj, I., Chehab, A., & Kayssi, A. (2018). IoT survey: An SDN and fog computing perspective. Computer Networks, 143, 221-246. doi:10.1016/j.comnet.2018.07.020
  • Singh, A., & Chatterjee, K. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79, 88-115. doi:10.1016/j.jnca.2016.11.027
  • Stojmenovic, I., & Wen, S. (2014). The Fog Computing Paradigm: Scenarios and Security Issues. 2, 1-8. doi:10.15439/2014f503
  • Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., & Yang, Q. (2017). Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities. IEEE Transactions on Industrial Informatics, 13(5), 2140-2150. doi:10.1109/tii.2017.2679740
  • Yannuzzi, M., Milito, R., Serral-Gracia, R., Montero, D., & Nemirovsky, M. (2014). Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing. 325-329. doi:10.1109/camad.2014.7033259
  • Yi, S., Hao, Z., Qin, Z., & Li, Q. (2015). Fog Computing: Platform and Applications. 73-78. doi:10.1109/HotWeb.2015.22
  • Yu, C., Songqing, C., Peng, H., & Brown, D. (2015). FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation. 2-11. doi:10.1109/nas.2015.7255196
  • Zhang, H., Xiao, Y., Bu, S., Niyato, D., Yu, R., & Han, Z. (2016). Fog computing in multi-tier data center networks: A hierarchical game approach. 1-6. doi:10.1109/icc.2016.7511146
  • Zhang, Y., Niyato, D., & Wang, P. (2015). Offloading in Mobile Cloudlet Systems with Intermittent Connectivity. IEEE Transactions on Mobile Computing, 14(12), 2516-2529. doi:10.1109/tmc.2015.2405539
  • Zhou, W., & Zhang, D. (1999). Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 46(1), 78-80. doi:10.1109/82.749102
There are 34 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Erdal Erdal 0000-0003-1174-1974

Publication Date January 31, 2019
Submission Date November 11, 2018
Published in Issue Year 2019 Volume: 11 Issue: 1

Cite

APA Erdal, E. (2019). Sis Bilişim Tabanlı İmza Doğrulama: Senaryoya Dayalı Bir Yaklaşım. International Journal of Engineering Research and Development, 11(1), 64-76. https://doi.org/10.29137/umagd.481464

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