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
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
Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled ...Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled and transmits data via email platform.Firstly,to address the impact of environmental factors such as weather and light on image quality,a deep neural network(DNN)image exposure correction network is employed to rectify images with abnormal exposure.Additionally,a back propagation(BP)neural network is utilized to fit a curve relating the brightness difference to the gamma value of images before and after exposure correction,thereby adjusting the gamma value of the camera.Secondly,to enhance the precision of YOLOv5 algorithm in differentiating between anomalies in nighttime imagery,infrared image data are employed,and a context-aware light-weight label assignment head and coordinate attention mechanism are incorporated into the model to augment the model’s detection accuracy and recall rate for small targets.Furthermore,to meet the demand for reporting of abnormal situations in unattended environments,an automatic target identification and reporting process has been designed which combines YOLOv5 algorithm with the frame-difference motion detection algorithm.The camera has been tested for compatibility with the current mainstream commercial email platforms.The mean time required for transmitting a single image file via the email platform is less than 10 s,while the mean time for transmitting a short video is less than 60 s.The BP network’s average training loss is 0.015,and the average testing loss is 0.013,which basically meets the precision requirements for gamma adjustment.The improved YOLOv5 algorithm achieved an mAP@0.5 of 91.5%and a recall rate of 85.5%,effectively enhancing the accuracy of small object detection.展开更多
Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable se...Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.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.
基金Funded by Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,China(No.GZKF-202219)。
文摘Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled and transmits data via email platform.Firstly,to address the impact of environmental factors such as weather and light on image quality,a deep neural network(DNN)image exposure correction network is employed to rectify images with abnormal exposure.Additionally,a back propagation(BP)neural network is utilized to fit a curve relating the brightness difference to the gamma value of images before and after exposure correction,thereby adjusting the gamma value of the camera.Secondly,to enhance the precision of YOLOv5 algorithm in differentiating between anomalies in nighttime imagery,infrared image data are employed,and a context-aware light-weight label assignment head and coordinate attention mechanism are incorporated into the model to augment the model’s detection accuracy and recall rate for small targets.Furthermore,to meet the demand for reporting of abnormal situations in unattended environments,an automatic target identification and reporting process has been designed which combines YOLOv5 algorithm with the frame-difference motion detection algorithm.The camera has been tested for compatibility with the current mainstream commercial email platforms.The mean time required for transmitting a single image file via the email platform is less than 10 s,while the mean time for transmitting a short video is less than 60 s.The BP network’s average training loss is 0.015,and the average testing loss is 0.013,which basically meets the precision requirements for gamma adjustment.The improved YOLOv5 algorithm achieved an mAP@0.5 of 91.5%and a recall rate of 85.5%,effectively enhancing the accuracy of small object detection.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:71-829-2024).
文摘Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.
