Blockchain-Based Certificateless Bidirectional Authenticated Searchable Encryption Scheme in Cloud Email System

Traditional email systems can only achieve one-way communication,which means only the receiver is allowed to search for emails on the email server.In this paper,we propose a blockchain-based certificateless bidirectional authenticated searchable encryption model for a cloud email system named certificateless authenticated bidirectional searchable encryption(CL-BSE)by combining the storage function of cloud server with the communication function of email server.In the new model,not only can the data receiver search for the relevant content by generating its own trapdoor,but the data owner also can retrieve the content in the same way.Meanwhile,there are dual authentication functions in our model.First,during encryption,the data owner uses the private key to authenticate their identity,ensuring that only legal owner can generate the keyword ciphertext.Second,the blockchain verifies the data owner’s identity by the received ciphertext,allowing only authorized members to store their data in the server and avoiding unnecessary storage space consumption.We obtain a formal definition of CL-BSE and formulate a specific scheme from the new system model.Then the security of the scheme is analyzed based on the formalized security model.The results demonstrate that the scheme achieves multikeyword ciphertext indistinguishability andmulti-keyword trapdoor privacy against any adversary simultaneously.In addition,performance evaluation shows that the new scheme has higher computational and communication efficiency by comparing it with some existing ones.

BUES: A Blockchain-Based Upgraded Email System

This paper focuses on the improvement of traditional email system architecture with the help of blockchain technology in the existing network environment. The improved system architecture can better improve the security and stability of the system. The email content is extracted and stored in the blockchain network to achieve regulatory traceability between the email service provider and the higher-level organization. In turn, A Blockchain-based Upgraded Email System(BUES) is proposed. The defects of the existing traditional email system are addressed. Firstly, the threat model of the traditional email system is analyzed, and solutions are proposed for various threats. Then a system architecture consisting of the blockchain network, email servers, and users are constructed. The implementation of BUES is carried out, and the related experimental process and algorithm steps are given. After the experimental analysis, it is shown that BUES can ensure the security, reliability, efficiency, and traceability of email transmission.

电子邮件·免费·3GB！——United Email Systems

前一段时期．GB级别的超大容量免费邮箱充斥在各大新闻媒体的报道中，这场以Gmail为领头羊的邮箱大战终于令众多老百姓得到了不少实惠，原以为近阶段1GB的免费邮箱应该是目前最大的容量了．但是最近却出了一个3GB的免费电邮：United Email Systems．笔者不禁心痒，立即将其注册试用一番。

Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT

Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as phishing.This study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks.The study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron algorithms.The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching 0.99.This study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.

Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in cyberattacks.Though the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the victims.In this background,there is a drastic increase observed in the number of phishing emails sent to potential targets.This scenario necessitates the importance of designing an effective classification model.Though numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the literature.The current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)model.The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones.At the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word elimination.Then,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature vectors.Moreover,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing emails.Furthermore,the CS algorithm is used to fine-tune the parameters involved in the GRU model.The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several dimensions.Extensive comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches.The proposed model achieved a maximum accuracy of 99.72%.

An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing

In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We introduce an autonomous function for a server to generate training examples, in which double-bounce emails are automatically collected and their class labels are given by a crawler-type software to analyze the website maliciousness called SPIKE. In general, since spammers use botnets to spread numerous malicious emails within a short time, such distributed spam emails often have the same or similar contents. Therefore, it is not necessary for all spam emails to be learned. To adapt to new malicious campaigns quickly, only new types of spam emails should be selected for learning and this can be realized by introducing an active learning scheme into a classifier model. For this purpose, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with a data selection function. In RAN-LSH, the same or similar spam emails that have already been learned are quickly searched for a hash table in Locally Sensitive Hashing (LSH), in which the matched similar emails located in “well-learned” are discarded without being used as training data. To analyze email contents, we adopt the Bag of Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency (TF-IDF). We use a data set of double-bounce spam emails collected at National Institute of Information and Communications Technology (NICT) in Japan from March 1st, 2013 until May 10th, 2013 to evaluate the performance of the proposed system. The results confirm that the proposed spam email detection system has capability of detecting with high detection rate.

Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

Recently,developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives.It results in illegal access to users’private data and compromises it.Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data.Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity.This article focuses on the design of bio-geography based optimization with deep learning for Phishing Email detection and classification(BBODL-PEDC)model.The major intention of the BBODL-PEDC model is to distinguish emails between legitimate and phishing.The BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning,tokenization,and stop word elimination.Besides,TF-IDF model is applied for the extraction of useful feature vectors.Moreover,optimal deep belief network(DBN)model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning process.The performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several dimensions.Extensive comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches.

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns.Emails stay in the leading positions for business as well as personal use.This popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email assaults.Email filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the end-users.But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced.Thus,this study provides a solution related to email message body text automatic classification into phishing and email spam.This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email Classification.The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails.At the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation.Next,the SRRNN method can be useful in recognizing and classifying spam emails.As hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter optimizer.To investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

Business Email Compromise Challenges to Medium and Large-Scale Firms in USA: An Analysis

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.

Deep Neural Network Based Spam Email Classification Using Attention Mechanisms

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.

面向异常行为的邮件访问控制网关的设计与实现

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。

基于用户行为和网络拓扑的Email蠕虫传播

通过分析用户行为规律和电子邮件网络拓扑特征,提出了能够准确描述Email蠕虫传播特性的仿真算法。首先,基于Enron Email数据集构建了能够表征Email网络特点的仿真环境,实验结果验证了仿真算法能够准确地体现Email蠕虫传播特性。然后从理论和实验两方面分析验证了关键节点被感染后会加速蠕虫的传播的结论,并讨论了不同防护措施对蠕虫传播的抑制效果。最后建立了Email蠕虫传播数学解析模型(Topo-SIS),与仿真结果的比较表明,该模型能够较好地预测Email蠕虫的传播规模。

基于OPNET的Email流量建模研究及仿真

网络仿真是一种全新的网络规划、设计和分析技术,它能够验证实际方案的有效性和比较多个不同的设计方案,为网络的规划设计提供可靠的定量依据。针对网络仿真中的关键问题—流量仿真与建模,对网络仿真软件OPNET的流量建模机制进行了研究分析,并就网络仿真中Email业务前景流量数学模型的建立做了研究,最后基于OPNET平台对仿真模型进行了仿真验证。

一个利用EMAIL实现分布数据共享的方法

文章介绍一种利用Email实现分布数据共享的方法。文中先对该方法的几个要点进行了描述，之后介绍了该方法的设计和实现过程，最后介绍了它在好来西CIMS系统中的应用。

利用接触跟踪机制实现Email蠕虫的检测

针对Email蠕虫逐渐成为一种主要的网络威胁,提出基于接触跟踪机制检测蠕虫的方法CTCBF。该方法利用"差分熵"对单个网络节点的异常连接行为进行检测,再通过异常节点之间的连接关系利用跟踪算法建立跟踪链,当跟踪链的长度达到设定阈值时,跟踪链上的可疑节点被确认为感染节点。针对阈值的不确定性,提出了一种动态阈值方法,根据不同的网络感染等级自适应调整阈值大小。仿真试验表明,该方法能够快速、准确地检测出蠕虫的传播行为,同时为未知蠕虫的检测提供了一种新的模式。

Email挖掘系统的体系模型及其具体实现

本文介绍了一种Email挖掘系统的体系模型,阐述了此模型的各个模块所涉及的相关算法和技术,着重论述了标准词库建立、文本内容分析和Email信息聚类等的具体方法,并通过一个实例详细论述了Email挖掘的应用过程。

利用设计模式进行Email系统的设计

简单介绍了设计模式的概念和ErichGamma归纳的一些基本的面向对象软件设计模式.根据Email系统的特点,将这些模式应用到系统的整体设计中,使得系统结构清晰协调,保证了系统的稳定性和可扩充性.

论email在英语写作教学中的辅助作用

本文分析了学生在英语写作方面所存在的问题,依据现代写作理论,对email辅助英语写作教学进行了探讨。Email辅助写作教学活动不仅激发了学生的写作积极性,提高了学生的写作能力,而且扩大了学生的知识面,提高了学生的跨文化交际能力。

一种基于SVM和领域综合特征的Email自动分类方法

Email自动分类已成为半结构化文本信息自动处理的研究热点。本文在对已有Email自动分类方法深入研究的基础上,提出了一种基于SVM和领域综合特征的Email自动分类方法。主要包括一是将SVM引入到Email自动分类研究中,并对SVM学习算法中的核函数和参数选择进行了探讨;二是鉴于词频的特征表示方法难以准确表示Email主要内容,因此将领域知识引入Email特征表示中,并在此基础上提出了一种综合领域知识和词频的特征表示方法,用于Email分类。该方法是在词频特征的基础上加入人工总结出的领域特征,从而更能准确地表示Email的主要内容,以提高Email分类的平均F-score。通过实验,验证了基于SVM和领域综合特征的Email自动分类方法能有效地提高Email自动分类处理的准确性。

一个基于Email的数据交换模型

提出一种具有平台独立性的基于Email的5层数据交换模型,以实现在异构的网络、操作系统和数据库管理系统之间交换数据。分析了该模型的工作原理和使用环境,分层设计并实现了该模型,最后给出了具体应用实例。