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Abstract

Phishing has been one of the most successful attacks in recent years. Criminals are motivated by increasing financial gain and constantly improving their email phishing methods. A key goal, therefore, is to develop effective detection methods to cope with huge volumes of email data. In this paper, a solution using BLSTM neural network and FastText word embeddings has been proposed. The solution uses preprocessing techniques like stop-word removal, tokenization, and padding. Two datasets were used in three experiments: balanced and imbalanced, whereas in the imbalanced dataset, the effect of maximum token size was investigated. Evaluation of the model indicated the best metrics: 99.12% accuracy, 98.43% precision, 99.49% recall, and 98.96% f1-score on the imbalanced dataset. It was compared to an existing solution that uses the DL model and word embeddings. Finally, the model and solution architecture were implemented as a browser plug-in.
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Authors and Affiliations

Rafał Wolert
1
Mariusz Rawski
1

  1. Institute of Telecommunications, Faculty of Electronics and Information Technology, Warsaw University of Technology, Poland
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Abstract

The COVID-19 pandemic is accompanied by a cyber pandemic, involving changes in the modi operandi of perpetrators of various crimes, and an infodemic, associated with the spread of disinformation. The article analyses the impact of the COVID-19 pandemic on cybercrime and presents the latest research on the number of cybercrime cases in Poland and their growth dynamics. It determines the factors that contribute to the commission of a crime and prevent easy identification of criminals. It also suggests the legal and organisational changes that could reduce the number and effects of the most frequently recorded cyberattacks at a time of COVID-19. Particular attention is paid to legal problems of the growing phenomenon of identity theft, and the need to ensure better protection of users from phishing, including through education and proactive security measures consisting in blocking Internet domains used for fraudulent attempts to obtain data and financial resources.
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Bibliography

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Authors and Affiliations

Agnieszka Gryszczyńska

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