Cognitive biases are commonly used by attackers to manipulate users’psychology in phishing emails.This study systematically analyzes the exploitation of cognitive biases in phishing emails and addresses the following...Cognitive biases are commonly used by attackers to manipulate users’psychology in phishing emails.This study systematically analyzes the exploitation of cognitive biases in phishing emails and addresses the following questions:(1)Which cognitive biases are frequently exploited in phishing emails?(2)How are cognitive biases exploited in phishing emails?(3)How effective are cognitive bias features in detecting phishing emails?(4)How can the exploitation of cognitive biases in phishing emails be modelled?To address these questions,this study constructed a cognitive processing model that explains how attackers manipulate users by leveraging cognitive biases at different cognitive stages.By annotating 482 phishing emails,this study identified 10 common types of cognitive biases and developed corresponding detection models to evaluate the effectiveness of these bias features in phishing email detection.The results show that models incorporating cognitive bias features significantly outperform baseline models in terms of accuracy,recall,and F1 score.This study provides crucial theoretical support for future anti-phishing methods,as a deeper understanding of cognitive biases offers key insights for designing more effective detection and prevention strategies.展开更多
In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative...In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.展开更多
The average office worker receives around 80 emails each day.With that volume of mail,individual messages can easily get overlooked.Follow these simple rules to get your emails noticed and replied to.
一、本刊刊登语言学及相关学科的学术论文。本刊刊登英文稿。二、本刊实行匿名审稿制。作者投稿时,请在另页写明姓名、性别(民族——汉族可省略)、出生年、籍贯、学位、职称、研究方向、工作单位、通信地址、邮政编码、联系电话、电子信...一、本刊刊登语言学及相关学科的学术论文。本刊刊登英文稿。二、本刊实行匿名审稿制。作者投稿时,请在另页写明姓名、性别(民族——汉族可省略)、出生年、籍贯、学位、职称、研究方向、工作单位、通信地址、邮政编码、联系电话、电子信箱(E-mail)。获得基金资助产出的文章,还应注明基金项目的名称和项目编号。如有多位作者,请注明通讯作者。三、请通过本刊采编系统(https://yyyj.cbpt.cnki.net)投稿。必要时可向编辑部Email(yyyj1981@126.com)寄送电子文稿(word格式和pdf格式各1份)。四、来稿应按国家标准使用简化字、标点符号和数字。古文字,请作者自制图片文件插入正文中,字图高度0.4厘米。图片、表格要求高精度黑白格式,勿使用灰度或彩色图片。音标符号,请使用Times New Roman或IPAPANNEW字体(函索即寄),勿使用不符合Unicode规范的自制音标。展开更多
To address the current situation where many small enterprises lack efficient management of customer data, this paper proposes a design and implementation plan of a customer relationship management (CRM) system based o...To address the current situation where many small enterprises lack efficient management of customer data, this paper proposes a design and implementation plan of a customer relationship management (CRM) system based on email service. It aims to solve the problems of data dispersion, untimely update and information redundancy in customer management of small and medium-sized enterprises. The system includes four core functional modules: historical email analysis, lead pool management, customer management and email archiving. Through email mining and web crawler technology, the system can extract potential customer information from historical emails and enrich lead data;the lead pool management module supports lead information maintenance, status tracking and conversion of high-value leads;the customer management module realizes the maintenance and dynamic tracking of customer information;the email management module provides the archiving of emails and attachments and the structured storage of basic email information. The system provides automated and intelligent customer information management, improves the work efficiency of sales staff, and provides an efficient customer relationship management solution for enterprises.展开更多
文摘Cognitive biases are commonly used by attackers to manipulate users’psychology in phishing emails.This study systematically analyzes the exploitation of cognitive biases in phishing emails and addresses the following questions:(1)Which cognitive biases are frequently exploited in phishing emails?(2)How are cognitive biases exploited in phishing emails?(3)How effective are cognitive bias features in detecting phishing emails?(4)How can the exploitation of cognitive biases in phishing emails be modelled?To address these questions,this study constructed a cognitive processing model that explains how attackers manipulate users by leveraging cognitive biases at different cognitive stages.By annotating 482 phishing emails,this study identified 10 common types of cognitive biases and developed corresponding detection models to evaluate the effectiveness of these bias features in phishing email detection.The results show that models incorporating cognitive bias features significantly outperform baseline models in terms of accuracy,recall,and F1 score.This study provides crucial theoretical support for future anti-phishing methods,as a deeper understanding of cognitive biases offers key insights for designing more effective detection and prevention strategies.
文摘In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.
文摘The average office worker receives around 80 emails each day.With that volume of mail,individual messages can easily get overlooked.Follow these simple rules to get your emails noticed and replied to.
文摘一、本刊刊登语言学及相关学科的学术论文。本刊刊登英文稿。二、本刊实行匿名审稿制。作者投稿时,请在另页写明姓名、性别(民族——汉族可省略)、出生年、籍贯、学位、职称、研究方向、工作单位、通信地址、邮政编码、联系电话、电子信箱(E-mail)。获得基金资助产出的文章,还应注明基金项目的名称和项目编号。如有多位作者,请注明通讯作者。三、请通过本刊采编系统(https://yyyj.cbpt.cnki.net)投稿。必要时可向编辑部Email(yyyj1981@126.com)寄送电子文稿(word格式和pdf格式各1份)。四、来稿应按国家标准使用简化字、标点符号和数字。古文字,请作者自制图片文件插入正文中,字图高度0.4厘米。图片、表格要求高精度黑白格式,勿使用灰度或彩色图片。音标符号,请使用Times New Roman或IPAPANNEW字体(函索即寄),勿使用不符合Unicode规范的自制音标。
文摘To address the current situation where many small enterprises lack efficient management of customer data, this paper proposes a design and implementation plan of a customer relationship management (CRM) system based on email service. It aims to solve the problems of data dispersion, untimely update and information redundancy in customer management of small and medium-sized enterprises. The system includes four core functional modules: historical email analysis, lead pool management, customer management and email archiving. Through email mining and web crawler technology, the system can extract potential customer information from historical emails and enrich lead data;the lead pool management module supports lead information maintenance, status tracking and conversion of high-value leads;the customer management module realizes the maintenance and dynamic tracking of customer information;the email management module provides the archiving of emails and attachments and the structured storage of basic email information. The system provides automated and intelligent customer information management, improves the work efficiency of sales staff, and provides an efficient customer relationship management solution for enterprises.
