Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2293
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dc.contributor.authorNair, Adarsh S V-
dc.contributor.authorAchary, Rathnakar-
dc.date.accessioned2023-12-09T08:56:05Z-
dc.date.available2023-12-09T08:56:05Z-
dc.date.issued2023-
dc.identifier.citationpp. 1-5en_US
dc.identifier.isbn9781665456272-
dc.identifier.urihttps://doi.org/10.1109/ICAIA57370.2023.10169678-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2293-
dc.description.abstractOne of the weakest links in any security system is neither the devices used nor the programs running on them; but the human beings using these devices. Most cyberattacks are initiated by human error. Hackers always use the most accessible and effective social engineering techniques to attack. Simply put, it is the art of manipulating people into sharing sensitive and confidential information. This research proposes a framework with four modules, namely, a source analyzer, a content classifier and analyzer, a link analyzer, and a risk reporting module, as a social engineering defender system for categorizing the risks before the email reaches the inbox of the user. Before it reaches the end user's inbox, the system blocks the emails the social engineering defender has marked as 'very high risk'. © 2023 IEEE.en_US
dc.language.isoenen_US
dc.publisher2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023en_US
dc.subjecthuman emotionsen_US
dc.subjectphishing attacken_US
dc.subjectrisk analysisen_US
dc.subjectrisk reportingen_US
dc.subjectrisk scoreen_US
dc.subjectsocial engineeringen_US
dc.titleSocial Engineering Defender (Se.Def): Human Emotion Factor Based Classification and Defense Against Social Engineering Attacksen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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