骚扰电话监控系统的研究与设计①

骚扰电话监控系统是一个对骚扰电话进行监控和拦截的系统,它对减少骚扰电话。提高电话用户的使用体验有着很重要的意义。首先给出了骚扰电话监控系统的组网方案和总体架构。然后重点设计了骚扰电话监控系统的监控和拦截的流程。     

基于互联网安全对诈骗、骚扰电话异常呼叫的拦截处置方案

近年来,诈骗电话、骚扰电话等事件频发,尤其是部分案件已经对广大人民群众生命财产安全以及正常的生产生活秩序造成了严重的危害和影响,基础电信企业有必要也有责任对异常呼叫进行及时拦截处置,从源头上减少诈骗、骚扰电话对广大人民群众的滋扰。基于此,笔者分析当今诈骗、骚扰电话的形势,提出信令拦截系统建设的必要性。方案实施验证了拦截效果,可为进一步净网行动提供参考。     

固定电话智能拦截系统设计及策略分析

基于不法分子使用群拨呼叫或VolP(网络电话)技术手段实施诈骗的技术特点,本文设计了固定电话智能拦截系统,提出一种固定电话智能拦截策略,通过在智能拦截系统中预设一段语音来检测来电是否录音电话,辅以陌生号码二次接通、白名单黑名单、联网查询、按键监控等综合措施来判断和拦截骚扰电话。本方法可以自主识别群拨打来电和录音形式来电,智能联网查询来电号码,对陌生来电进行通话实时监控并全程录音,用户发生按键操作时语音提醒,可以有效抗录音诈骗和群拨呼叫,保证用户免受骚扰和诈骗电话的威肋、。     

某地移动骚扰电话整治总结

现在移动用户经常收到的陌生号码的呼叫,表现为振铃后便挂断电话,等待回拨后提示中奖或六合彩的诈骗电话,一般表现为周期内呼叫大量用户,且未接通的电话为骚扰电话。骚扰电话给用户带来了很多伤害,同时也给运营商带来很多的麻烦,除了让用户提高警惕和在手机上设置屏蔽,运营商也可以借助MSC的强大处理能力拦截骚扰电话。     

拒绝垃圾短信骚扰电话瑞星手机安全软件全搞定

经常被垃圾短信、骚扰电话骚扰,已经成为困扰手机用户的焦点问题,目前市场上拦截垃圾短信和骚扰电话的软件很多,但它们使用起来比较麻烦。而瑞星新推出的手机安全软件具有非常全面的安全功能,其中防骚扰功能可以全面解决这些,一款软件全部解决。     

iPhone用户拦截骚扰初体验 360手机卫士

不久前,360手机卫士i OS苹果商店版发布重大更新,新增骚扰电话识别功能,苹果手机用户无需越狱就能通过360手机卫士识别来电是否为骚扰电话。骚扰拦截功能上线一周时间,苹果手机用户共标记骚扰号码110万次,近20万个号码被用户"手工"标记。骚扰电话、诈骗电话、广告推销高居标记榜前三,在标记量最高的前十个号码中有六个都是400疑似诈骗电话,占比最大。     

基于云安全的骚扰电话过滤系统研究

智能手机用户对通信安全的需求日益增加,本文研究目前骚扰电话过滤系统现状,提出一种基于云安全技术的设计方案,对其中的技术做了深入的研究。该系统将过滤服务器集群和大量手机整合到一个云安全体系中,实现对骚扰电话的快速和有效过滤。     

基于正则表达式的WebMail监控与审计

对WebMail的监控是对电子邮件监控的一个难点.提出并实现了一种基于正则表达式的邮件监控系统.该系统使用Winsock2 SPI技术,截取本地计算机发送的网络数据包,采用正则表达式对发送数据包的内容进行实时分析,判断其中是否含有发送Email的行为,通过在线拦截和离线审计相结合,实现了对WebMail的监控与审计.实验测试的结果验证了该系统的可行性和有效性.     

基于HOOK技术的机房监控系统

HOOK（钩子）技术具有相当强大的功能,通过它可以对几乎所有的windows系统消息和事件进行拦截、监视、处理。本文阐述了基于HOOK技术实现机房监控系统,对HOOK机制的应用有一定的参考价值。     

基于云计算环境下的企业网络信息监控平台设计

针对目前企业日益突出的网络信息安全问题,本文设计一个基于云计算技术的企业网络信息监控系统。网络信息监控系统整体功能包括数据包拦截捕获、数据流向控制、数据分析保存、端口扫描、进程监控、选择性授权、查询统计和管理八大模块。提出了基于云计算技术的企业网络监控体系方案,并对云处理层进行详细设计,为网络信息安全的管理工作提供新的思路。     

基于判别模型的垃圾邮件过滤方法

垃圾邮件泛滥已成为网络时代的一个重要问题,随着垃圾邮件的伪装技术的不断更新,以前主要的几种垃圾邮过滤技术面临着新的挑战。文中提出一种新的基于判别模型的垃圾邮件过滤方法,邮件分类器通过不断的学习来更新特征项的权重,当新的信息到达时,计算所有特征项的权重之和,并将其转化为一个概率值,如果此概率值超过某一阈值时,就认定此信息为垃圾邮件;同时将此方法应用到实时邮件处理环境中。实验结果表明,此方法能明显地提高准确度,有效地降低误判率。     

ADVS: a reputation-based model on filtering SPIT over P2P-VoIP networks

Interconnection among VoIP networks over the Internet is being an important trend. Therefore, distributed and self-organized VoIP networks are emerging as the times require. Because of VoIP’s cheapness and simplicity, many institutes forecast that it emerges as next spam entryway, like e-mail service. Especially, interconnected VoIP networks are self-organized and lack centralized authority. In this paper, we propose a novel scheme, called ADVS (Anti-Distributed Voice Spam), against SPIT (SPam over Internet Telephony) on the distributed and self-organized VoIP networks. ADVS presents a proper reputation model to evaluate end-user’s past behavior and accumulate other users’ referrals for detecting and filtering spam calls. In the VoIP network, ADVS acts as a middleware of VoIP proxy and constructs a DHT-based P2P network over call proxies and part of P2P-VoIP client to interlink each other and share end-user’s reputation. At the end of the paper, we verify AVDS through building simulation test bed. The results of experiment show that ADVS could detect spam calls accurately and stably.     

Genetic optimized artificial immune system in spam detection: a review and a model

Spam is a serious universal problem which causes problems for almost all computer users. This issue affects not only normal users of the internet, but also causes a big problem for companies and organizations since it costs a huge amount of money in lost productivity, wasting users’ time and network bandwidth. Many studies on spam indicate that spam cost organizations billions of dollars yearly. This work presents a machine learning method inspired by the human immune system called Artificial Immune System (AIS) which is a new emerging method that still needs further exploration. Core modifications were applied on the standard AIS with the aid of the Genetic Algorithm. Also an Artificial Neural Network for spam detection is applied with a new manner. SpamAssassin corpus is used in all our simulations.     

基于链接相似度和作弊系数的Spam网页识别算法

Spam网页主要通过链接作弊手段达到提高搜索排名而获利的目的,根据链接作弊的特征,引入链接相似度和作弊系数两个指标来判定网页作弊的可能性。借鉴BadRank算法思想,从Spam网页种子集合通过迭代计算链接相似度和作弊系数,并根据与种子集合的链接指向关系设置权重,将待判定的网页进行度量。最后选取Anti-Trust Rank等算法作对比实验,结果验证了本文算法在准确率和适应性方面优于对比算法。     

Personalized email management at network edges

A new technique for managing and disseminating Web-based email prefetches messages and generates dynamic pages, displaying them at the network edge. Compared to other popular Web-based email servers, the prefetching and caching emails (PACE) prototype shows an improved performance with respect to user-perceived latency. Additionally, PACE'S centralized neural-network-based personalized spam filter will filter spam and viruses at the server's origin, thus saving bandwidth. Another major concern for users is the email accounts being clogged with spam. Spam filters can be classified as server-side or client-side. Server-side filters are integrated with email servers and filter out spam at the server end.     

Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

Online reviews regarding purchasing services or products offered are the main source of users’ opinions. To gain fame or profit, generally, spam reviews are written to demote or promote certain targeted products or services. This practice is called review spamming. During the last few years, various techniques have been recommended to solve the problem of spam reviews. Previous spam detection study focuses on English reviews, with a lesser interest in other languages. Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced. Thus, this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit (SRD-OSGRU) on Arabic Opinion Text. The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes: spam and truthful. Initially, the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format. Next, unigram and bigram feature extractors are utilized. The SGRU model is employed in this study to identify and classify Arabic spam reviews. Since the trial-and-error adjustment of hyperparameters is a tedious process, a white shark optimizer (WSO) is utilized, boosting the detection efficiency of the SGRU model. The experimental validation of the SRD-OSGRU model is assessed under two datasets, namely DOSC dataset. An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.     

垃圾邮件过滤技术研究综述*

垃圾邮件过滤是一种主动安全防御技术。首先概述了垃圾邮件过滤的发展历史及其基本概念;然后根据不同的标准对垃圾邮件过滤技术进行了分类,并评述了各种垃圾邮件过滤方法和技术;最后展望了垃圾邮件过滤技术及其产品的发展方向。     

垃圾邮件来源追踪技术研究及应用

从SMTP协议入手,分析垃圾邮件制造者利用SMTP协议的缺陷隐藏邮件来源的方法,然后通过对电子邮件结构中邮件信头的详细分析,从邮件信头读取垃圾邮件的相关信息来追踪垃圾邮件的来源。