Factors Triggering Phishing Attacks During The Covid-19 Pandemic

Background Information

The unprecedented outbreak of covid-19 globally has sent panic and fear worldwide. Therefore, all institutions had to shut down so that individual states could find the best way to forge forward to understand the extent of the pandemic (Gerald, 2020). Once testing of the various was tested, there was a transmission of the virus at a community level. Therefore, government institutions and private institutions decided to operate from home. Therefore, this leads to installing Wi-Fi and the application of remote desktop control in doing their jobs.

Although remote work had many challenges, which was even more detrimental as data conveyed would transmit data packets from one to another that could be susceptible to attacks by the digital assailant. Reports have demonstrated during covid19era, that voluminous attacks took place which was approximated to 245,771 phishing attacks in only one month. This was reported by the Anti-phishing working group (APWG). The UK’s HMRC demonstrated that 73% raise in email phishing attacks in the covid-19 era (Hamdani, 2021).

The research was animated by the rising number of phishing attacks during the covid19era. The level of phishing attacks was minimal before covid. By considering this fact, therefore was needed to understand what the sudden cause of this kind of phenomenon is.

The research attempts to solve What are the factors that trigger the upsurge of phishing attacks in the covid 19 situation? When covid-19 struck, there was fear about what it was and its adaptability to avoid shrinking the economy. Adopting online services has increased the surface area of attacks as fake phishing websites could be sent to individuals, hence infiltrating personal information.

To mitigate the problem identified and review through literature review, data from open-source websites such as phishtank was downloaded. The downloaded data was used to devise a model that will help us identify the causes of attacks in covid 19 era. Additionally, the Microsoft threat modeling tool will be used to establish the veracity of the in anticipation of mitigating the problem domain.

This paper will contribute a lot to determining how to deal with fear and our data records during the ravaging pandemic.

The following are some of the critical aspects that the paper will articulate.

• To identify the causes of attacks and how to deal with these kinds of problems in the future when they happen.
• The Research will establish how to prevent our data from scams.
• The research will prioritize how such attacks can be dealt with in future environments and to get awareness about which mechanism hackers are using to launch these attacks.

Phishing attacks have existed for a long time, especially in social engineering mechanisms. The identity of an individual is stolen through duping (Thomas, 2020). Various mechanism has been applied to eradicate and detect phishing attacks.

Problem Statement

Pattern matching filters have become a salient technique in detecting malware. Site data and URLs are matched with certain data contained in our recommender system (Collins et al., 2021). If it matches, it will be identified that the cause of attack had been used there before. A technique of pattern matching has been proposed by scholars such as (Rahamathunnisa et al., 2017). in analyzing malicious patterns of phishing attacks.

Important work has been carried out to establish how attacks using URLs have been carried out and demonstrated by a number of scholars how phishing URLs can be determined (Azeez et al., 2021, p.102328). The key element is extracted from the URL, such as the natural language process, and a prediction is made (Somesha, 2020). This was achieved by training the model and using a random classifier to predict the causes of attacks.

The application of content-based to predict the causes of phishing attacks as opposed to the earlier URL mechanism (Ozker, 2020). This mechanism has demonstrated better performance than other methods of determining phishing attacks (Mao et al., 2019). This method focused on entirety analyzing the content of the page and the metadata of the search engines.

The application of AdaBoost and multi-boost to detect phishing websites are used by the researcher for comparison purposes such that they ensemble learners to improve the presentation and calculations (Mallick et al., p.28). It is noted that once this classifier is used in research, it will improve precision.

Deep learning, which is the concept of artificial intelligence, has been used in modeling many things in the computing field, and phishing attack detection is not an exception (Sarker et al., 2021). Deep machine learning has been used in simulating intrusion detection systems (Mathews,2019, p.1269). The figure below illustrates forecasting of phishing attacks or legal traffic, a batch of input data that is supplied to the neurons and given weights (Basit et al.,2021,p.144).

The proposal for the development of an intelligent phishing detection system is a system used to classify the site as either legitimate or a phishing website (Yadollahi et al., 2019). The application of unique machine learning models in predicting all these. The mechanism applied the use of random forest in classification in order to enhance the F-score and accuracy of the model in attacks determination (Sahingoz, 2020).

1. What are the security measures taken to prevent our data from phishing assaults?
2. What are the causes to be victims of attacks and how many individuals are affected due to attacks in the covid period?
3. What are the mechanisms perpetrators of attacks are using to launch attacks on emails, URLs, or covid domain names?

Comparative analysis studies will be conducted in a bid to answer the research questions which will be analyzed using content analysis. Moreover, experimental designs will also be carried out such as the decision tree algorithm, and Microsoft threat analysis tool.

The upsurge of covid-19 has been on the rise and has created a lot of concerns through which individual data has been stolen through phishing attacks. 

To mitigate the causes of attacks, a model will be developed such that we can be able to predict what the causes of attacks are. This will be implemented by the development of a decision-tree algorithm that will be used to train the model and test the data (Charbuty, 2021).

A Microsoft threat analysis tool will be used, to model the causes of attacks and possible mitigation strategies to enhance the security of our data from phishing assaults.

These are the approaches going to undertake in order to perform prediction which is achieved using a decision tree algorithm.

This research will not involve any other persons except the supervisor who provides the guidance and other required support. There is no disclosing of collected personal data. This research has no public financial support, and data will be collected from open-source free websites and research journals.

I am totally aware of and comply with the classification as stated in the ethics form that is provided by my university.

References:

1. Azeez, N.A., Misra, S., Margaret, I.A. and Fernandez-Sanz, L., 2021. Adopting automated whitelist approach for detecting phishing attacks. Computers & Security, 108, p.102328.
2. Basit, A., Zafar, M., Liu, X. et al. A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommun Syst 76, 139–154 (2021).
3. Collins, B., Hoang, D.T., Nguyen, N.T. and Hwang, D., 2021. Trends in combating fake news on social media–a survey. Journal of Information and Telecommunication, 5(2), pp.247-266.
4. Charbuty, B. and Abdulazeez, A., 2021. Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), pp.20-28.
5. Gerald, E., Obianuju, A. and Chukwunonso, N., 2020. Strategic agility and performance of small and medium enterprises in the phase of Covid-19 pandemic. International Journal of Financial, Accounting, and Management, 2(1), pp.41-50.
6. Somesha, M., Pais, A.R., Rao, R.S. and Rathour, V.S., 2020. Efficient deep learning techniques for the detection of phishing websites. S?dhan?, 45(1), pp.1-18.
7. Mallick, J., Talukdar, S., Alsubih, M., Ahmed, M., Islam, A.R.M.T., Shahfahad and Thanh, N.V., 2021. Proposing receiver operating characteristic-based sensitivity analysis with introducing swarm optimized ensemble learning algorithms for groundwater potentiality modelling in Asir region, Saudi Arabia. Geocarto International, pp.1-28.
8. Mao, J., Bian, J., Tian, W., Zhu, S., Wei, T., Li, A. and Liang, Z., 2019. Phishing page detection via learning classifiers from page layout feature. EURASIP Journal on Wireless Communications and Networking, 2019(1), pp.1-14.
9. Rahamathunnisa, U., Manikandan, N., Kumaran, U.S. and Niveditha, C., 2017. Preventing from phishing attack by implementing url pattern matching technique in web. International Journal of Civil Engineering and Technology, 8(9), pp.1200-1208.
10. Ozker, U. and Sahingoz, O.K., 2020, September. Content based phishing detection with machine learning. In 2020 International Conference on Electrical Engineering (ICEE)(pp. 1-6). IEEE.
11. Thomas, T., P Vijayaraghavan, A. and Emmanuel, S., 2020. Machine learning and cybersecurity. In Machine Learning Approaches in Cyber Security Analytics(pp. 37-47). Springer, Singapore.
12. Sarker, I.H., Furhad, M.H. and Nowrozy, R., 2021. Ai-driven cybersecurity: an overview, security intelligence modeling and research directions. SN Computer Science, 2(3), pp.1-18.
13. Sahingoz, O.K., Buber, E., Demir, O. and Diri, B., 2019. Machine learning based phishing detection from URLs. Expert Systems with Applications, 117, pp.345-357.
14. Mathews, S.M., 2019, July. Explainable artificial intelligence applications in NLP, biomedical, and malware classification: a literature review. In Intelligent computing-proceedings of the computing conference(pp. 1269-1292). Springer, Cham.
15. Hamdani, K.J. and Mustafa, M.I.E., 2021. Effectiveness of Online Anti-Phishing Delivery methods in raising Awareness among Internet Users.
16. Yadollahi, M.M., Shoeleh, F., Serkani, E., Madani, A. and Gharaee, H., 2019, April. An adaptive machine learning based approach for phishing detection using hybrid features. In 2019 5th International Conference on Web Research (ICWR)(pp. 281-286). IEEE

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