{"title":"X-Phishing-Writer: A Framework for Cross-Lingual Phishing Email Generation","authors":"Shih-Wei Guo, Yao-Chung Fan","doi":"10.1145/3670402","DOIUrl":null,"url":null,"abstract":"<p>Cybercrime is projected to cause annual business losses of $10.5 trillion by 2025, a significant concern given that a majority of security breaches are due to human errors, especially through phishing attacks. The rapid increase in daily identified phishing sites over the past decade underscores the pressing need to enhance defenses against such attacks. Social Engineering Drills (SEDs) are essential in raising awareness about phishing, yet face challenges in creating effective and diverse phishing email content. These challenges are exacerbated by the limited availability of public datasets and concerns over using external language models like ChatGPT for phishing email generation. To address these issues, this paper introduces X-Phishing-Writer, a novel cross-lingual Few-Shot phishing email generation framework. X-Phishing-Writer allows for the generation of emails based on minimal user input, leverages single-language datasets for multilingual email generation, and is designed for internal deployment using a lightweight, open-source language model. Incorporating Adapters into an Encoder-Decoder architecture, X-Phishing-Writer marks a significant advancement in the field, demonstrating superior performance in generating phishing emails across 25 languages when compared to baseline models. Experimental results and real-world drills involving 1,682 users showcase a 17.67% email open rate and a 13.33% hyperlink click-through rate, affirming the framework’s effectiveness and practicality in enhancing phishing awareness and defense.</p>","PeriodicalId":54312,"journal":{"name":"ACM Transactions on Asian and Low-Resource Language Information Processing","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Asian and Low-Resource Language Information Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3670402","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Cybercrime is projected to cause annual business losses of $10.5 trillion by 2025, a significant concern given that a majority of security breaches are due to human errors, especially through phishing attacks. The rapid increase in daily identified phishing sites over the past decade underscores the pressing need to enhance defenses against such attacks. Social Engineering Drills (SEDs) are essential in raising awareness about phishing, yet face challenges in creating effective and diverse phishing email content. These challenges are exacerbated by the limited availability of public datasets and concerns over using external language models like ChatGPT for phishing email generation. To address these issues, this paper introduces X-Phishing-Writer, a novel cross-lingual Few-Shot phishing email generation framework. X-Phishing-Writer allows for the generation of emails based on minimal user input, leverages single-language datasets for multilingual email generation, and is designed for internal deployment using a lightweight, open-source language model. Incorporating Adapters into an Encoder-Decoder architecture, X-Phishing-Writer marks a significant advancement in the field, demonstrating superior performance in generating phishing emails across 25 languages when compared to baseline models. Experimental results and real-world drills involving 1,682 users showcase a 17.67% email open rate and a 13.33% hyperlink click-through rate, affirming the framework’s effectiveness and practicality in enhancing phishing awareness and defense.
期刊介绍:
The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to:
-Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc.
-Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc.
-Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition.
-Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc.
-Machine Translation involving Asian or low-resource languages.
-Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc.
-Information Extraction and Filtering: including automatic abstraction, user profiling, etc.
-Speech processing: including text-to-speech synthesis and automatic speech recognition.
-Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc.
-Cross-lingual information processing involving Asian or low-resource languages.
-Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.