Phishing Image Spam Classification Research Trends: Survey and Open Issues

Abari, Ovye John; Fazlida, Nor; Khalid, Fatimah; Yunus, Mohd Sidek Fadhil Mohd; Afiza, Noor

doi:10.14569/ijacsa.2020.0111196

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Cited by 2 publications

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References 36 publications

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“…The authors [22] used datasets like Personal Collection, Dredze, and Spam Archives for image spam classification. Low-level metadata and image metadata are two common feature sets.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Detection of Text and Image Spam Using Cost-Sensitive Deep Learning

Mallampati,

Hegde

2024

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“…The authors [22] used datasets like Personal Collection, Dredze, and Spam Archives for image spam classification. Low-level metadata and image metadata are two common feature sets.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Detection of Text and Image Spam Using Cost-Sensitive Deep Learning

Mallampati,

Hegde

2024

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How to Understand Phishing

Buřita

2023

Wseas Transactions on Computers

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The article is based on the results of previous research, is focused on the analysis and classification of phishing emails, and documents the results of communication with the phisher attacker. In the first part of the article, an experiment carried out with a randomly selected set of emails confirms the considerable uncertainty of the correct result of automatic classification based on keywords using text analysis software. The second part of the article contains the experiment of communication with phishing attackers. A typical scenario of message exchange is presented. Thanks to the correct setting of security and protection rules, no security incident occurred. The literature search confirms the great interest in publishing in the field of phishing. Compared to the content of the published article, it turned out that its focus is completely original.

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Phishing Image Spam Classification Research Trends: Survey and Open Issues

Cited by 2 publications

References 36 publications

Enhanced Detection of Text and Image Spam Using Cost-Sensitive Deep Learning

Enhanced Detection of Text and Image Spam Using Cost-Sensitive Deep Learning

How to Understand Phishing

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