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
Spam emails pose a threat to individuals. The proliferation of spam emails daily has rendered traditional machine learning and deep learning methods for screening them ineffective and inefficient. In our research, we ...Spam emails pose a threat to individuals. The proliferation of spam emails daily has rendered traditional machine learning and deep learning methods for screening them ineffective and inefficient. In our research, we employ deep neural networks like RNN, LSTM, and GRU, incorporating attention mechanisms such as Bahdanua, scaled dot product (SDP), and Luong scaled dot product self-attention for spam email filtering. We evaluate our approach on various datasets, including Trec spam, Enron spam emails, SMS spam collections, and the Ling spam dataset, which constitutes a substantial custom dataset. All these datasets are publicly available. For the Enron dataset, we attain an accuracy of 99.97% using LSTM with SDP self-attention. Our custom dataset exhibits the highest accuracy of 99.01% when employing GRU with SDP self-attention. The SMS spam collection dataset yields a peak accuracy of 99.61% with LSTM and SDP attention. Using the GRU (Gated Recurrent Unit) alongside Luong and SDP (Structured Self-Attention) attention mechanisms, the peak accuracy of 99.89% in the Ling spam dataset. For the Trec spam dataset, the most accurate results are achieved using Luong attention LSTM, with an accuracy rate of 99.01%. Our performance analyses consistently indicate that employing the scaled dot product attention mechanism in conjunction with gated recurrent neural networks (GRU) delivers the most effective results. In summary, our research underscores the efficacy of employing advanced deep learning techniques and attention mechanisms for spam email filtering, with remarkable accuracy across multiple datasets. This approach presents a promising solution to the ever-growing problem of spam emails.展开更多
高校邮件系统平均每月面临数万次的暴力破解认证攻击,攻击者会使用简单邮件传输协议(Simple Mail Transfer Protocal,SMTP)认证的方式对高校师生邮件账号进行暴力破解认证,尤其是分布式暴力破解和低频慢速暴力破解难以识别检测,是导致...高校邮件系统平均每月面临数万次的暴力破解认证攻击,攻击者会使用简单邮件传输协议(Simple Mail Transfer Protocal,SMTP)认证的方式对高校师生邮件账号进行暴力破解认证,尤其是分布式暴力破解和低频慢速暴力破解难以识别检测,是导致邮件服务器面临资源消耗及账户安全问题的巨大威胁。因此,有必要设计一种面向异常行为的邮件访问控制网关,通过分析邮件日志捕获异常攻击行为,动态阻断恶意互联网协议(Internet Protocal,IP)攻击。测试结果表明,该网关通过分析邮件日志、抽取安全事件、捕获异常行为特征,构建了特征规则;基于漏桶算法捕获低频、分布式暴力破解的恶意IP,联动防火墙实现了对恶意IP的动态封禁及解除;设计、实现访问控制网关并应用于校园网,成功阻断了62%的攻击流量。展开更多
Mobile agent currently is a hot spot among research fields of Internet technology. The deployment of mo-bile agents over network usually needs extra infrastructure for agent migration and communication,which adds to t...Mobile agent currently is a hot spot among research fields of Internet technology. The deployment of mo-bile agents over network usually needs extra infrastructure for agent migration and communication,which adds to thedifficulty of popularizing MA systems. We present in this paper an Email-box-based mechanism of agent migrationand communication,which is built on top of the formerly developed MOON-EAMS system. This mechanism,basedon Email formatting skills,utilizes Email for data transfer,and offers a loosely coupled option of agent migration andcommunication,which ,compared to related works ,obtains the advantage of easy implementation,and reduces the riskof network connection failure.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.
文摘Spam emails pose a threat to individuals. The proliferation of spam emails daily has rendered traditional machine learning and deep learning methods for screening them ineffective and inefficient. In our research, we employ deep neural networks like RNN, LSTM, and GRU, incorporating attention mechanisms such as Bahdanua, scaled dot product (SDP), and Luong scaled dot product self-attention for spam email filtering. We evaluate our approach on various datasets, including Trec spam, Enron spam emails, SMS spam collections, and the Ling spam dataset, which constitutes a substantial custom dataset. All these datasets are publicly available. For the Enron dataset, we attain an accuracy of 99.97% using LSTM with SDP self-attention. Our custom dataset exhibits the highest accuracy of 99.01% when employing GRU with SDP self-attention. The SMS spam collection dataset yields a peak accuracy of 99.61% with LSTM and SDP attention. Using the GRU (Gated Recurrent Unit) alongside Luong and SDP (Structured Self-Attention) attention mechanisms, the peak accuracy of 99.89% in the Ling spam dataset. For the Trec spam dataset, the most accurate results are achieved using Luong attention LSTM, with an accuracy rate of 99.01%. Our performance analyses consistently indicate that employing the scaled dot product attention mechanism in conjunction with gated recurrent neural networks (GRU) delivers the most effective results. In summary, our research underscores the efficacy of employing advanced deep learning techniques and attention mechanisms for spam email filtering, with remarkable accuracy across multiple datasets. This approach presents a promising solution to the ever-growing problem of spam emails.
文摘高校邮件系统平均每月面临数万次的暴力破解认证攻击,攻击者会使用简单邮件传输协议(Simple Mail Transfer Protocal,SMTP)认证的方式对高校师生邮件账号进行暴力破解认证,尤其是分布式暴力破解和低频慢速暴力破解难以识别检测,是导致邮件服务器面临资源消耗及账户安全问题的巨大威胁。因此,有必要设计一种面向异常行为的邮件访问控制网关,通过分析邮件日志捕获异常攻击行为,动态阻断恶意互联网协议(Internet Protocal,IP)攻击。测试结果表明,该网关通过分析邮件日志、抽取安全事件、捕获异常行为特征,构建了特征规则;基于漏桶算法捕获低频、分布式暴力破解的恶意IP,联动防火墙实现了对恶意IP的动态封禁及解除;设计、实现访问控制网关并应用于校园网,成功阻断了62%的攻击流量。
文摘Mobile agent currently is a hot spot among research fields of Internet technology. The deployment of mo-bile agents over network usually needs extra infrastructure for agent migration and communication,which adds to thedifficulty of popularizing MA systems. We present in this paper an Email-box-based mechanism of agent migrationand communication,which is built on top of the formerly developed MOON-EAMS system. This mechanism,basedon Email formatting skills,utilizes Email for data transfer,and offers a loosely coupled option of agent migration andcommunication,which ,compared to related works ,obtains the advantage of easy implementation,and reduces the riskof network connection failure.
