Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads
With the development of internet technology, email has become the formal communication method in modern society. Email often contains a large amount of personal privacy information, possible business agreements, and sensitive attachments, which make emails a good target for hackers. One of the most...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/13/4425 |
| _version_ | 1797564026159366144 |
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| author | Yong Fang Yijia Xu Peng Jia Cheng Huang |
| author_facet | Yong Fang Yijia Xu Peng Jia Cheng Huang |
| author_sort | Yong Fang |
| collection | DOAJ |
| description | With the development of internet technology, email has become the formal communication method in modern society. Email often contains a large amount of personal privacy information, possible business agreements, and sensitive attachments, which make emails a good target for hackers. One of the most common attack method used by hackers is email XSS (Cross-site scripting). Through exploiting XSS vulnerabilities, hackers can steal identities, logging into the victim’s mailbox and stealing content directly. Therefore, this paper proposes an email XSS detection model based on deep learning technology, which can identify whether the XSS payload is carried in the email or not. Firstly, the model could extract the Sender, Receiver, Subject, Content, Attachment field information from the original email. Secondly, the email XSS corpus is formed after data processing. The Word2Vec algorithm is introduced to train the corpus and extract features for each email sample. Finally, the model uses the Bidirectional-RNN algorithm and Attention mechanism to train the email XSS detection model. In the experiment, the AUC (area under curve) value of the Bidirectional-RNN model reached 0.9979. When the Attention mechanism was added, the accuracy upper limit of the Bidirectional-RNN model was raised to 0.9936, and the loss value was reduced to 0.03. |
| first_indexed | 2024-03-10T18:51:34Z |
| format | Article |
| id | doaj.art-9ded29475242453f8a0e35a39b7dc0d9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:51:34Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9ded29475242453f8a0e35a39b7dc0d92023-11-20T05:06:53ZengMDPI AGApplied Sciences2076-34172020-06-011013442510.3390/app10134425Providing Email Privacy by Preventing Webmail from Loading Malicious XSS PayloadsYong Fang0Yijia Xu1Peng Jia2Cheng Huang3College of Cybersecurity, Sichuan University, Chengdu 610065, ChinaCollege of Cybersecurity, Sichuan University, Chengdu 610065, ChinaCollege of Cybersecurity, Sichuan University, Chengdu 610065, ChinaCollege of Cybersecurity, Sichuan University, Chengdu 610065, ChinaWith the development of internet technology, email has become the formal communication method in modern society. Email often contains a large amount of personal privacy information, possible business agreements, and sensitive attachments, which make emails a good target for hackers. One of the most common attack method used by hackers is email XSS (Cross-site scripting). Through exploiting XSS vulnerabilities, hackers can steal identities, logging into the victim’s mailbox and stealing content directly. Therefore, this paper proposes an email XSS detection model based on deep learning technology, which can identify whether the XSS payload is carried in the email or not. Firstly, the model could extract the Sender, Receiver, Subject, Content, Attachment field information from the original email. Secondly, the email XSS corpus is formed after data processing. The Word2Vec algorithm is introduced to train the corpus and extract features for each email sample. Finally, the model uses the Bidirectional-RNN algorithm and Attention mechanism to train the email XSS detection model. In the experiment, the AUC (area under curve) value of the Bidirectional-RNN model reached 0.9979. When the Attention mechanism was added, the accuracy upper limit of the Bidirectional-RNN model was raised to 0.9936, and the loss value was reduced to 0.03.https://www.mdpi.com/2076-3417/10/13/4425WebmailXSSWord2Vecdeep learningAttention mechanism |
| spellingShingle | Yong Fang Yijia Xu Peng Jia Cheng Huang Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads Applied Sciences Webmail XSS Word2Vec deep learning Attention mechanism |
| title | Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads |
| title_full | Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads |
| title_fullStr | Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads |
| title_full_unstemmed | Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads |
| title_short | Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads |
| title_sort | providing email privacy by preventing webmail from loading malicious xss payloads |
| topic | Webmail XSS Word2Vec deep learning Attention mechanism |
| url | https://www.mdpi.com/2076-3417/10/13/4425 |
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