Abstract
As a containment strategy for the dreaded Corona Virus Disease 19 (COVID 19) which is spreading rapidly and causing severe damage to life and economy of nations, places of public gathering like schools, places of religious worship, open physical markets, offices as well as venues for social meetings (such as clubs) are been closed down, to promote social distancing in most nations across the globe. Therefore, most public/private organizations, and even individuals have resorted to the use of diverse Information Technologies (IT) for connecting themselves and other life essentials. Educational, agricultural, religious and even health institutions now deliver their services to users/clients and receive payments via online platforms, students study from home, even employees of most organizations now work remotely (maybe from their homes). Moreover, there is a sharp growth in demand for food deliveries and online grocery. The massive adoption of IT by almost all aspects of human life especially during this epidemic has also led to increased cyber security concerns. Cybercriminals and other individuals with malicious intent now take COVID-19 as an opportunity to perpetrate cybercrimes, especially for monetary gains. Domestic violence seems to be on the rise perhaps due to the lockdown, contact tracing approaches are massively been developed and used, healthcare systems are being attacked with ransom ware and resources such as patient records confidentiality, and integrity are being compromised. Individuals are falling victim to phishing attacks through COVID-19 related content. This paper presents an extensive study of major cyber security concerns that are and could take place during the COVID 19 pandemic as well as strategies for mitigating them.
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References
Tong, T.R.: Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV). Perspect. Med. Virol. 16, 43–95 (2006). https://doi.org/10.1016/S0168-7069(06)16004-8
Tsang, K.W.: Severe Acute Respiratory Syndrome (SARS). Int. Encycl. Public Health 691–695 (2008). https://doi.org/10.1016/B978-012373960-5.00219-7
Cheng, V.C., Lau, S.K., Woo, P.C., Yuen, K.Y.: Severe acute respiratory syndrome coronavirus as an agent of emerging and reemerging infection. Clin. Microbiol. Rev. 20(4), 660–694 (2007). https://doi.org/10.1128/CMR.00023-07
Oh, M.D., Park, W.B., Park, S.W., Choe, P.G., Bang, J.H., Song, K.H., Kim, E.S., Kim, H.B., Kim, N.J.: Middle East respiratory syndrome: what we learned from the 2015 outbreak in the Republic of Korea. Korean J. Intern. Med. 33(2), 233–246 (2008). https://doi.org/10.3904/kjim.2018.031
Aleanizy, F.S., Mohmed, N., Alqahtani, F.Y., El Hadi Mohamed, R.A.: Outbreak of Middle East respiratory syndrome coronavirus in Saudi Arabia: a retrospective study. BMC Infect. Dis. 17(1), 23 (2007). https://doi.org/10.1186/s12879-016-2137-3
Sandhya, R.: Public health experts worry about the spread of COVID-19 misinformation. https://www.rollcall.com/2020/03/18/public-health-experts-worry-aboutspread-of-COVID-19-misinformation/. Published in 2020
Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media. ACM SIGKDD Explor. Newsl. (2017). https://doi.org/10.1145/3137597.3137600
Busari, S., Adebayo, B.: Nigeria records chloroquine poisoning after Trump endorses it for coronavirus treatment. CNN. https://www.cnn.com/2020/03/23/africa/chloroquine-trump-nigeria-intl/index.html. Published 2020
Joe, W.: Coronavirus: Indian man “died by suicide” after becoming convinced he was infected. The Telegraph. https://www.telegraph.co.uk/global-health/science-and-disease/coronavirus-indian-man-died-suicide-becoming-convinced-infected/. Published 2020
Spencer, S.H.: False claims of nationwide lockdown for COVID-19.https://www.factcheck.org/2020/03/false-claims-of-nationwide-lockdown-for-covid-19/
Kasulis, K.: Patient 31 and South Korea’s sudden spike in coronavirus cases. Aljazeera. https://www.aljazeera.com/news/2020/03/31-south-korea-sudden-spike-coronavirus-cases-200303065953841.html
The Verge. Major tech platforms say they’re ‘jointly combating fraud and misinformation’ about COVID-19. https://www.theverge.com/2020/3/16/21182726/coronavirus-covid-19-facebook-google-twitter-youtube-joint-effort-misinformation-fraud
Collinson, S., Khan, K., Heffernan, J.M.: The effects of media reports on disease spread and important public health measurements. PLoS ONE (2015). https://doi.org/10.1371/journal.pone.0141423
Pennycook, G., McPhetres, J., Zhang, Y., Rand, D.: Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy nudge intervention. PsyArXiv [Working Paper], 1–24 (2020). https://doi.org/10.31234/OSF.IO/UHBK9
Ahmad, T.: Corona virus (COVID-19) pandemic and work from home: challenges of cybercrimes and cyber-security. Available at SSRN 3568830, 2020—papers.ssrn.com
Gabriel, J.A., Egwuche, O.S.: Modeling employees’ activities of public service sector using production rules. In: Annals Computer Science Series 13th Tome 2nd Fasc, Romania, 2015
Iwasokun, G.B., Egwuche, O.S., Gabriel, A.J.: Neural network-based health personnel monitoring system. Afr. J. Comput. ICT IEEE 8(1), 79–87 (2015)
Roomp, K., Oliver, N.: ACDC-tracing: towards anonymous citizen-driven contact tracing. Arxiv 2020 preprint, available at https://arxiv.org/ftp/arxiv/papers/2004/2004.07463
Raskar, R., Schunemann, I., Barbar, R., Vilcans, K., Gray, J., Vepakomma, P., Kapa, S., Nuzzo, A., Gupta, R., Berke, A.: Apps gone rogue: maintaining personal privacy in an epidemic. arXiv preprint arXiv:2003.08567 (2020)
Gabriel, A.J., Alese, B.K., Adetunmbi, A.O., Adewale, O.S., Sarumi, O.A.: Post-quantum crystography system for secure electronic voting. Open Comput. Sci 9, 292–298 (2019). https://doi.org/10.1515/comp-2019-0018
Asher, A.I.: 11 countries are now using people’s phones to track the coronavirus pandemic, and it heralds a massive increase in surveillance. Accessed 15 May 2020. Online. Available: www.businessinsider.com/countriestracking-citizensphones-coronavirus-2020-3?r=DEIR=
Altuwaiyan, T., Hadian, M., Liang, X.: Epic: efficient privacy-preserving contact tracing for infection detection. In: 2018 IEEE International Conference on Communications (ICC), IEEE, pp. 1–6 (2018)
Tracetogether Accessed 23 April 2020. [Online]. Available https://www.tracetogether.gov.sg/
Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Reichert, L., Brack, S., Scheuermann, B.: Privacy-preserving contact tracing of covid-19 patients. In: 2020. Cryptology eprint archive. Available at https://eprint.iacr.org/2020/375.pdf
Brack, S., Reichert, L., Scheuermann, B.: Decentralized contact tracing using a dht and blind signatures. Cryptology eprint archive, 2020. Available at https://eprint.iacr.org/2020/398.pdf
Berke, A., Bakker, M., Vepakomma, P., Raskar, R., Larson, K., Pentland, A.: Assessing disease exposure risk with location histories and protecting privacy: a cryptographic approach in response to a global pandemic. arXiv preprint arXiv:2003.14412 (2020)
Diffie, W., Hellman, M.: New directions in cryptography. IEEE Trans. Inf. Theory 22(6), 644–654 (1976)
Vaudenay, S.: Analysis of DP-3T. Accessed: 23 April 2020. [Online]. Available: https://eprint.iacr.org/2020/399 (2020)
Alowolodu, O.D., Adelaja, G.K., Alese, B.K., Olayemi, O.C.: Medical image security using quantum cryptography. Issues Informing Sci. Inf. Technol. 15, 57–67 (2018)
Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H.: Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv (2020)
Cao, Y., Xu, Z., Feng, J., Jin, C., Han, X., Wu, H.: Longitudinal assessment of COVID-19 using a deep learning–based quantitative CT pipeline: illustration of two cases. Radiol. Cardiothorac. Imaging 2, e200082 (2020)
Huang, L., Han, R., Ai, T., Yu, P., Kang, H., Tao, Q.: Serial quantitative chest CT assessment of COVID-19: deep-learning approach. Radiol. Cardiothorac. Imaging 2, e200075 (2020)
Qi, X., Jiang, Z., Yu, Q., Shao, C., Zhang, H., Yue, H.: Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study. MedRxiv (2020)
Gozes, O., Frid-Adar, M., Greenspan, H., Browning, Y., Zhang, H., Ji, W.: Rapid AI development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection and patient monitoring using deep learning ct image analysis. arXiv:2003.05037 (2020)
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B.: Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 200905 (2020)
Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y.: Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study. MedRxiv (2020)
Jin, S. Wang, B. Xu, H. Luo, C. Wei, Y. Zhao, W., “AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks,” MedRxiv, 2020
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M.: Lung infection quantification of COVID-19 in CT images with deep learning. arXiv:2003.04655 (2020)
Tang, L., Zhang, X., Wang, Y., Zeng, X.: Severe COVID-19 Pneumonia: assessing inflammation burden with volume-rendered chest CT. Radiol. Cardiothorac. Imaging 2, e200044 (2020)
Shen, C., Yu, N., Cai, S., Zhou, J., Sheng, J., Liu, K.: Quantitative computed tomography analysis for stratifying the severity of coronavirus disease 2019. J. Pharm. Anal. (2020)
Ghoshal, B., Tucker, A.: Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv:2003.10769 (2020)
Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv:2003.10849 (2020)
Zhang, J., Xie, Y., Li, Y., Shen, C., Xia, Y.: COVID-19 screening on chest X-ray images using deep learning based anomaly detection. arXiv:2003.12338 (2020)
Wang, L., Wong, A.: COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv:2003.09871 (2020)
Jin, C., Cheny, W., Cao, Y., Xu, Z., Zhang, X., Deng, L.: Development and evaluation of an AI system for COVID-19 diagnosis. MedRxiv (2020)
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J.: A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). MedRxiv (2020)
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z.: Deep learning enables accurate diagnosis of novel Coronavirus (COVID-19) with CT images. MedRxiv (2020)
Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S.: Deep learning system to screen Coronavirus disease 2019 peumonia. arXiv:2002.09334 (2020)
Shi, F., Xia, L., Shan, F., Wu, D., Wei, Y., Yuan, H.: Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification. arXiv:2003.09860 (2020)
Tang, Z., Zhao, W., Xie, X., Zhong, Z., Shi, F., Liu, J.: Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. arXiv:2003.11988 (2020)
Wang, J.: Privacy-preserving recommender systems facilitated by the machine learning approach. PhD Thesis at the Université du Luxembourg (2018)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)
Bayardo, R.J., Agrawal, R.: Data privacy through optimal k- anonymization. In: ICDE 2005 Proceedings 21st International Conference on Data Engineering, 217–228. IEEE, 2005
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: ICDE 2007 IEEE 23rd International Conference on Data Engineering, 128, 106–115. IEEE (2007)
Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Hunt, T., Zhu, Z., Xu, Y., Peter, S., Ryoan, E.W.: A distributed and box for untrusted computation on secret data. In OSDI, 533–549 (2016)
Hynes, N., Cheng, R., Song, D.: Efficient deep learning on multi-source private data. arXiv preprint arXiv:1807.06689 (2018)
McKeen, F., Alexandrovich, I., Berenzon, A., Rozas, C.V., Shafi, H., Shanbhogue, V., Savagaonkar, U.R.: Innovative instructions and software model for isolated execution. HASP@ISCA 10 (2013)
Lam, S., Frankowski, D., Riedl, J.: Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In: Muller, G. (ed.) Emerging trends in information and communication security, volume 3995 of Lecture Notes in Computer Science, Ch. 2, p. 14, vol 29. Springer Berlin/Heidelberg, Berlin, Heidelberg (2006 Cited on page 36)
Foner, L.N.: Political artifacts and personal privacy: the yenta multi-agent distributed matchmaking system. PhD thesis, Program in Media Arts and Sciences, School of Architecture and Planning, Massachusetts Institute of Technology (June 1999, Cited on pages 4 and 36)
Williams, C.: Profile injection attack detection for securing collaborative recommender systems center for web intelligence. DePaul University School of Computer Science, Telecommunication, and Information Systems Chicago, Illinois, USA
Gabriel, A.J., Alese, B.K., Adetunmbi, A.O., Adewale, O.S.: Post-quantum crystography based security framework for cloud computing. J. Int. Technol. Secured Trans. 3(4), 344–350 (2014)
Gabriel, A.J., Alese, B.K., Adetunmbi, A.O., Adewale, O.S.: Post-quantum crystography: a combination of post-quantum cryptography and steganography. In: The 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013), Technically Co-sponsored by IEEE UK/RI Computer Chapter, 9th-12th Dec 2013, London, UK, pp. 454–457
Adebayo, O.T., Alese, B.K., Gabriel, A.J.: A model for computer worm detection in a computer network. Int. J. Comput. Appl. (0975–8887). 66(2), 22–28 (2013)
Alese, B.K., Gabriel, A.J., Olukayode, O., Daramola, O.A.: Modelling of risk management procedures for cybercrime control systems. In: The 2014 International Conference of Information Security and Internet Engineering, World Congress on Engineering, ISBN 978-988-19252-7-7; pp. 505–509. (United Kingdom, 2014)
Alese, B.K., Gabriel, A.J., Adetunmbi, A.O.: Design and implementation of internet protocol security filtering rules in a network environment. Int. J. Comput. Sci. Inf. Secur. USA 9(7), 134–143. Available at www.academia.edu (2011)
Bakken, D.E., Rarameswaran, R., Blough, D.M., Franz, A.A., Palmer, T.J.: Data obfuscation: anonymity and desensitization of usable data sets. IEEE Secur. Priv. 2(6), 34 (41, November 2004, Cited on page 39)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: International Conference on Management of Proceedings of the 2000 ACM SIGMOD
Gentry, C.: A fully homomorphic encryption scheme. PhD thesis, Stanford, CA, USA (2009). AAI3382729
Yao, A.C.-C.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science, 1986, pp. 162–167. IEEE, 1986
Braga, D.D., Niemann, M., Hellingrath, B., Neto, F.B.: Survey on computational trust and reputation models. ACM Comput. Surv. 51, 5, 101 (Nov. 2018, 40 pages). https://doi.org/10.1145/3236008
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Gabriel, A.J., Darwsih, A., Hassanien, A.E. (2021). Cyber Security in the Age of COVID-19. In: Hassanien, A.E., Darwish, A. (eds) Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches. Studies in Systems, Decision and Control, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-63307-3_18
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