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Spam-Detection with Comparative Analysis and Spamming Words Extractions

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Titel: Spam-Detection with Comparative Analysis and Spamming Words Extractions
Autoren: Md Khairul Islam, Md Al Amin, Md Rakibul Islam, Md Nosin Ibna Mahbub, Md Imran Hossain Showrov, Chetna Kaushal
Quelle: 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). :1-9
Verlagsinformationen: Institute of Electrical and Electronics Engineers (IEEE), 2021.
Publikationsjahr: 2021
Schlagwörter: FOS: Computer and information sciences, Review Spam, Information Systems and Management, Spamming, Forum spam, Social Sciences, 02 engineering and technology, Computer science, Malware, Detection and Prevention of Phishing Attacks, Information Overload in Knowledge Management and Work Performance, Decision Sciences, World Wide Web, Web Data Extraction and Crawling Techniques, Spam Detection, Electronic mail, Computer security, Spambot, Computer Science, Physical Sciences, 0202 electrical engineering, electronic engineering, information engineering, Internet privacy, Email Management, The Internet, Information Systems
Beschreibung: Communication through email plays an essential part especially in every sector of our day-to-day life. Considering its significance, it is important to filter spam emails from emails. Spam email, also known as junk email, is unwanted messages that are sent by the electronic medium in large quantities. Most of the spam emails are commercial in nature that is not only irritating but also harmful due to malicious scams or malware-hosting sites or use viruses attached to the message. In this paper, we identify spam emails and expose how spam emails can be distinguished from legitimate/normal emails. We deployed four machine learning models and two deep learning models over the datasets including the combined dataset. Besides, we also try to find the important keywords that are found repeatedly from spam emails repository. This type of knowledge will enable us to detect spam emails for our personnel and community security purpose.
Publikationsart: Article
Other literature type
DOI: 10.36227/techrxiv.16832320.v1
DOI: 10.1109/icrito51393.2021.9596218
DOI: 10.36227/techrxiv.16832320.v2
DOI: 10.36227/techrxiv.16832320
DOI: 10.60692/zd3h6-qe620
DOI: 10.60692/x7x3w-te124
Zugangs-URL: https://www.techrxiv.org/articles/preprint/Spam-Detection_with_Comparative_Analysis_and_Spamming_Words_Extractions/16832320/1/files/31127866.pdf
https://figshare.com/articles/preprint/Spam-Detection_with_Comparative_Analysis_and_Spamming_Words_Extractions/16832320/files/31503518.pdf
https://www.techrxiv.org/articles/preprint/Spam-Detection_with_Comparative_Analysis_and_Spamming_Words_Extractions/16832320/2/files/31503518.pdf
https://www.techrxiv.org/articles/preprint/Spam-Detection_with_Comparative_Analysis_and_Spamming_Words_Extractions/16832320
Rights: CC BY
IEEE Copyright
Dokumentencode: edsair.doi.dedup.....9d20f9f9d66d9fc771c68de788f27f4c
Datenbank: OpenAIRE
Beschreibung
Abstract:Communication through email plays an essential part especially in every sector of our day-to-day life. Considering its significance, it is important to filter spam emails from emails. Spam email, also known as junk email, is unwanted messages that are sent by the electronic medium in large quantities. Most of the spam emails are commercial in nature that is not only irritating but also harmful due to malicious scams or malware-hosting sites or use viruses attached to the message. In this paper, we identify spam emails and expose how spam emails can be distinguished from legitimate/normal emails. We deployed four machine learning models and two deep learning models over the datasets including the combined dataset. Besides, we also try to find the important keywords that are found repeatedly from spam emails repository. This type of knowledge will enable us to detect spam emails for our personnel and community security purpose.
DOI:10.36227/techrxiv.16832320.v1