Abstract
Internet plays dominant role amid Covid-19 pandemic as to meet day-to-day activities. As education system, financial transactions, businesses, and social gatherings started to function in online mode, leading to tremendous use of networks peaked to the level of cyberattacks. Simultaneously, the thirst for finding the data related to Covid-19 in order to take necessary precautions gave rise to huge risk of cyberattacks by browsing Covid-19 related websites, apps and falling into the trap of attackers risking the systems security. This research work considers the tweets related to cyberattacks and classifies using machine learning techniques and analyzes the impact of this pandemic. It was observed that support vector machine yielded high accuracy of 94% in classifying Covid-19, followed by decision tree with accuracy of 88% among other classifiers. The results were evaluated on different metrics like error rate, precision recall, and F-Score. SVM yielded high results among all.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
CPS: “Cybercrime—prosecution guidance”, The Crown Prosecution Service (CPS), Tech. Rep. (2019), https://www.cps.gov.uk/legal-guidance/cybercrimeprosecution-guidance. Accessed 17 Jun 2020
M. Cross, D.L. Shinder, Scene of the Cybercrime (Syngress, Boston, 2008)
S. Desai, New android app offers coronavirus safety mask but delivers SMS trojan (2020), https://www.zscaler.com/blogs/research/new-androidapp-offers-coronavirus-safety-mask-delivers-sms-trojan. Accessed 30 May 2020
Europol: Pandemic Profiteering: How Criminals Exploit COVID-19 Crisis (2020), https://www.europol.europa.eu/publicationsdocuments/pandemic-profiteering-how-criminalsexploit-covid-19-crisis. Accessed 15 Jun 2020
S. Gallagher, A. Brandt, Facing down the Myriad Threats Tied to covid19 (2020), https://news.sophos.com/enus/2020/04/14/covidmalware. Accessed 9 May 2020
Hiscox: The hiscox cyber readiness report 2019 (2019), https://www.hiscox.co.uk/cyberreadiness. Accessed 9 May 2020
G. Hulten, L. Spencer, P. Domingos, Mining time-changing data streams, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2001), pp. 97–106
O. Jain, M. Gupta, S. Satam, S. Panda, Has the COVID-19 pandemic affected the susceptibility to cyberbullying in India? Computers in Human Behavior Reports 2, 100029 (2020) ISSN 2451-9588
B. Koenig, Covid SMS Phishing Attempt (2020), https://twitter.com/BigBenKoenig/status/1242503232527589376. Accessed 30 May 2020
R. Kohavi, Scaling up the accuracy of Naïve-Bayes classifiers: A decision tree hybrid, in Proceedings of KDD-96, Portland, USA, (1996), pp. 202–207
Krebs on Security: Live Coronavirus Map Used to Spread Malware (2020), https://krebsonsecurity.com/2020/03/live-coronavirusmap-used-to-spread-malware/. Accessed 15 Jun 2020
N. Kumaran, S. Lugani, Protecting businesses against cyber threats during covid-19 and beyond (2020), https://cloud.google.com/blog/products/identitysecurity/protecting-against-cyber-threats-during-covid19-and-beyond. Accessed 17 Jun 2020
H.S. Lallie, L.A. Shepherd, J.R.C. Nurse, A. Erola, G. Epiphaniou, C. Maple, X. Bellekens, Cyber Security in the Age of COVID-19: A Timeline and Analysis of Cyber-Crime and Cyber-Attacks during the Pandemic, 2020
MalwareBytes: Cybercriminals impersonate World Health Organization to distribute fake coronavirus-book (2020), https://blog.malwarebytes.com/socialengineering/2020/03/cybercriminals-impersonate-worldhealth-organization-to-distribute-fake-coronavirus-ebook/. Accessed 15 Jun 2020
M. McGuire, S. Dowling, “Chapter 1: Cyberdependent crimes,” Home Office, Tech. Rep. (2013a), https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/246751/horr75-chap1.pdf. Accessed 18 Jun 2020
M. McGuire, S. Dowling, “Chapter 2: Cyber-enabled crimes—fraud and theft,” Home Office, Tech. Rep. (2013b), https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/248621/horr75-chap2.pdf. Accessed 18 Jun 2020
NHS: 10 tips to help if you are worried about coronavirus (2020), https://www.nhs.uk/oneyou/every-mindmatters/coronavirus-covid-19-anxiety-tips. Accessed 9 May 2020
Norton: Coronavirus phishing emails: How to protect against COVID-19 scams (2020), https://us.norton.com/internetsecurity-online-scamscoronavirus-phishing-scams.html. Accessed 15 Jun 2020
J.R.C. Nurse, Cybercrime and you: How criminals attack and the human factors that they seek to exploit, in The Oxford Handbook of Cyberpsychology, (OUP, Oxford, 2019)
T.L. O’Brien, Covid aid scams and dodgy 17 deals could have been avoided (2020), https://www.bloomberg.com/opinion/articles/2020-05-01/coronavirus-trillions-in-aid-draws-scams-anddodgy-deals. Accessed 9 May 2020
J.R. Quinlan, C4.5 Programs for Machine Learning (Morgan Kaufmann, Burlington, MA, 1993)
J. Rodger, The school meals coronavirus text scam which could trick parents out of thousands (2020), https://www.birminghammail.co.uk/news/midlandsnews/school-meals-coronavirus-text-scam-17975311. Accessed 30 May 2020
K. Sadaf, J. Sultana, Intrusion detection based on autoencoder and isolation forest in fog computing. IEEE Access 8, 167059–167068 (2020). https://doi.org/10.1109/ACCESS.2020.3022855
F. Shi, Threat spotlight: Coronavirus-related phishing (2020), https://blog.barracuda.com/2020/03/26/threatspotlight-coronavirus-related-phishing. Accessed 9 May 2020
R. Smithers, Fraudsters use bogus NHS contact-tracing app in phishing scam (2020), https://www.theguardian.com/world/2020/may/13/fraudsters-use-bogus-nhs-contact-tracing-app-inphishing-scam. Accessed 30 May 2020
J. Sultana, A.K. Jilani, Predicting breast cancer using logistic regression and multi-class classifiers. Int. J. Eng. Technol. 7(4.20), 22–26 (2018)
J. Sultana, N. Sultana, K. Yadav, F. Alfayez, Prediction of sentiment analysis on educational data based on deep learning approach, in Proceedings of the 21st Saudi Computer Society National Computer Conference (NCC), vol. 1, (2018)
J. Sultana, K. Sadaf, A.K. Jilani, R. Alabdan, Diagnosing breast cancer using support vector machine and multi-classifiers, in 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, (2019), pp. 449–451. https://doi.org/10.1109/ICCIKE47802.2019.9004356
The Guardian: US Authorities battle surge in coronavirus scams, from phishing to fake treatments (2020), https://www.theguardian.com/world/2020/mar/19/coronavirus-scams-phishing-fake-treatments. Accessed 15 Jun 2020
The Times: Fraudsters impersonate airlines and Tesco in coronavirus scams (2020), https://www.thetimes.co.uk/article/fraudstersimpersonate-airlines-and-tesco-in-coronavirus-scams5wdwhxq7p. Accessed 15 Jun 2020
K. Tysiac, How cybercriminals prey on victims of natural disasters (2018), https://www.journalofaccountancy.com/news/2018/sep/cyber-criminals-prey-on-natural-disaster-victims201819720.html. Accessed 9 May 2020
V.N. Vapnik, The Nature of Statistical Learning Theory, 2nd edn. (Springer-Verlag, New York, 1998)
WHO: WHO Coronavirus disease (Covid-19) dashboard (2020a), https://covid19.who.int/. Accessed 15 Jun 2020
WHO: “#healthyathome” (2020b), https://www.who.int/news-room/campaigns/connectingthe-world-to-combat-coronavirus/healthyathome. Accessed 9 May 2020
Wired: Hackers are targeting hospitals crippled by coronavirus (2020), https://www.wired.co.uk/article/coronavirus-hackerscybercrime-phishing. Accessed 15 Jun 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sultana, J., Jilani, A.K. (2021). Classifying Cyberattacks Amid Covid-19 Using Support Vector Machine. In: Bhardwaj, A., Sapra, V. (eds) Security Incidents & Response Against Cyber Attacks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-69174-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-69174-5_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69173-8
Online ISBN: 978-3-030-69174-5
eBook Packages: EngineeringEngineering (R0)