Business Email Compromise (BEC) attacks have emerged as a significant cybersecurity threat, leading to substantial financial losses for organizations. According to the FBI’s Internet Crime Complaint Center (IC3), BEC...Business Email Compromise (BEC) attacks have emerged as a significant cybersecurity threat, leading to substantial financial losses for organizations. According to the FBI’s Internet Crime Complaint Center (IC3), BEC attacks resulted in financial losses exceeding $1.8 billion in the USA in 2019 alone. Business Email Compromise (BEC) attacks have emerged as a significant cybersecurity threat, leading to substantial financial losses for organizations. According to the FBI’s Internet Crime Complaint Center (IC3), BEC attacks resulted in financial losses exceeding $1.8 billion in the USA in 2019 alone. BEC attacks target a wide range of sectors. No industry is immune to these attacks, which emphasizes the need for increased vigilance across all sectors. Attackers often impersonate high-level executives or vendors to gain credibility and manipulate employees into complying with fraudulent requests. BEC attacks have a global reach, with threat actors operating from various countries, including Nigeria, Russia, China, and Eastern European nations. We will examine the unique difficulties SMEs encounter in relation to BEC attacks. This study provides a more excellent knowledge of the severity of the problem and offers ideas for efficient mitigation solutions through an investigation of attack characteristics, tactics, and impacts.展开更多
Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional ...Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information systems.BERT’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing detection.Achieving an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing emails.This approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.展开更多
文摘Business Email Compromise (BEC) attacks have emerged as a significant cybersecurity threat, leading to substantial financial losses for organizations. According to the FBI’s Internet Crime Complaint Center (IC3), BEC attacks resulted in financial losses exceeding $1.8 billion in the USA in 2019 alone. Business Email Compromise (BEC) attacks have emerged as a significant cybersecurity threat, leading to substantial financial losses for organizations. According to the FBI’s Internet Crime Complaint Center (IC3), BEC attacks resulted in financial losses exceeding $1.8 billion in the USA in 2019 alone. BEC attacks target a wide range of sectors. No industry is immune to these attacks, which emphasizes the need for increased vigilance across all sectors. Attackers often impersonate high-level executives or vendors to gain credibility and manipulate employees into complying with fraudulent requests. BEC attacks have a global reach, with threat actors operating from various countries, including Nigeria, Russia, China, and Eastern European nations. We will examine the unique difficulties SMEs encounter in relation to BEC attacks. This study provides a more excellent knowledge of the severity of the problem and offers ideas for efficient mitigation solutions through an investigation of attack characteristics, tactics, and impacts.
基金supported by a grant from Hong Kong Metropolitan University (RD/2023/2.3)supported Princess Nourah bint Abdulrah-man University Researchers Supporting Project number (PNURSP2024R 343)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia for funding this research work through the project number“NBU-FFR-2024-1092-09”.
文摘Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information systems.BERT’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing detection.Achieving an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing emails.This approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.
