论文标题
通过利用深层神经学习者来建模生成的电子邮件中的相干性
Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners
论文作者
论文摘要
先进的机器学习和自然语言技术使攻击者能够发起基于社会工程的精致和有针对性的攻击。为了应对主动攻击者问题，研究人员此后一直诉诸主动的检测方法。使用有针对性的电子邮件欺骗受害者的电子邮件是一种先进的攻击方法。但是，自动文本生成需要控制生成内容的上下文和相干性，这已被确定为越来越困难的问题。所使用的方法利用了一个分层深神经模型，该模型使用输入文档中句子的学会表示来生成结构化的书面电子邮件。我们使用深层模型演示了简短和有针对性的短信的产生。使用定性研究以及多种定量措施评估合成文本的全球相干性。
Advanced machine learning and natural language techniques enable attackers to launch sophisticated and targeted social engineering-based attacks. To counter the active attacker issue, researchers have since resorted to proactive methods of detection. Email masquerading using targeted emails to fool the victim is an advanced attack method. However automatic text generation requires controlling the context and coherency of the generated content, which has been identified as an increasingly difficult problem. The method used leverages a hierarchical deep neural model which uses a learned representation of the sentences in the input document to generate structured written emails. We demonstrate the generation of short and targeted text messages using the deep model. The global coherency of the synthesized text is evaluated using a qualitative study as well as multiple quantitative measures.
