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Short description of the full variant dataset: Total number of instances: 88,647. Number of legitimate website instances (labeled as 0): 58,000. Number of phishing website instances (labeled as 1): 30,647. Total number of features: 111 (without target)
Refresh. Detecting Phishing Emails by Text Analytics.
Jan 5, 2024 · We have curated 11 datasets. The Nazario and Nigerian Fraud datasets contain only phishing emails. Cite this dataset: A. I. Champa, M. F. Rabbi, and M. F. Zibran, “Why phishing emails escape detection: A closer look at the failure points,” in 12th International Symposium on Digital Forensics and Security (ISDFS), 2024, pp. 1–6 (to appear). or
Sep 13, 2023 · We have curated 11 datasets spanning from 1998 to 2022. If you use this datasets, please cite: A. I. Champa, M. F. Rabbi, and M. F. Zibran, “Why phishing emails escape detection: A closer look at the failure points,” in 12th Interna- tional Symposium on Digital Forensics and Security (ISDFS), 2024, pp. 1–6 (to appear). Bibtext:
The first one is a phishing email corpus 3 containing more than 2000 phishing emails in a single text file of 400.000 lines in the mbox format. Every email in this dataset is a reported and verified phishing attempt.
Dec 1, 2020 · This paper presents two dataset variations that consist of 58,645 and 88,647 websites labeled as legitimate or phishing and allow the researchers to train their classification models, build phishing detection systems, and mining association rules.
Feb 21, 2024 · In this research paper, we present an optimized, fine-tuned transformer-based DistilBERT model designed for the detection of phishing emails. In the detection process, we work with a phishing email dataset and utilize the preprocessing techniques to clean and solve the imbalance class issues.
Email phishing datasets can be used to train and evaluate deep learning-based phishing detection systems and to research the characteristics and methods used in phishing attacks. 4.2.2. Benign Email Dataset
Nov 5, 2023 · The authors suggested that the methodology could be improved by combining phishing and non-phishing emails to establish a unique dataset that reflects real-life scenarios where fraudsters continuously evolve their techniques.
Phishing attacks perform by sending forged emails looking legitimate from an authentic entity to a victim or a group of victims [2][3]. They aim at obtaining users’ confidential data or uploading malware on their machines.
