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1.
电子邮件蠕虫是引起因特网中垃圾邮件泛滥的原因之一,严重威胁着因特网。该文提出了一种无标度网络上电子邮件蠕虫的传播模型,它通过用户检查邮件的频率和打开邮件附件的概率来描述邮件用户的行为,仿真了蠕虫在无标度网络和随机网络中的传播。结果表明,蠕虫在无标度网络中的传播速度比在随机网络中快得多,对进一步研究蠕虫的防御具有重要的意义。  相似文献   

2.
无尺度网络上的蠕虫传播行为研究   总被引:1,自引:0,他引:1  
许多网络如因特网、人类间的社会关系和物种之间的食物链网络等,都是无尺度网络。研究无尺度网络,对于防范黑客攻击、防治蠕虫的传播等都具有重要的意义.本文对随机网络和无尺度网络做了比较.重点介绍无尺度网络上蠕虫传播行为的特性——阈值和强韧性,最后指出了蠕虫防治的方法。  相似文献   

3.
分析了基于无尺度易感应用网络的拓扑蠕虫的传播特性,包括其感染整个应用网络所需要的传播时间和其在传播过程中对相关主机和网络资源的占用情况等。通过与扫描蠕虫相比较,分析出该类拓扑蠕虫传播时间更短,并且在传播过程中具有更好的隐蔽性,在实施最终攻击前很难被检测,从而使其对网络和主机具有更大威胁。针对这种威胁,文章提出了几种用于检测和防御基于无尺度网络应用拓扑蠕虫的可能方法。  相似文献   

4.
研究电子邮件蠕虫传播行为真实仿真问题.电子邮件蠕虫是造成电子邮件垃圾邮件泛滥的最主要的原因之一,使得计算机网络效率急剧下降,系统资源遭到严重破坏.为了能更好的对电子邮件蠕虫行为进行仿真,提出了一种基于用户行为和电子邮件网络拓扑结构相结合的电子邮件蠕虫传播模型方法,通过用户打开和检查邮件的概率来描述电子邮件用户的行为.方法能准确仿真蠕虫在网络拓扑中的传播.仿真结果表明,提出的新的蠕虫传播行为仿真方法能更加有效检测蠕虫传播特性,并对比分析了几组因素对蠕虫传播的影响,对进一步研究蠕虫的防御具有重要的意义.  相似文献   

5.
近几年,蠕虫频繁爆发,已成为互联网安全的主要威胁。为了清楚地理解蠕虫所造成的威胁,很有必要对蠕虫进行分类,以便进行深入的研究。该文对目前蠕虫传播模型进行了深入地研究,主要包括随机网络和无尺度网络上的模型。指出在构建无尺度网络时现有算法的一个共同缺陷——没有考虑链路的成本。最后预测了蠕虫的发展趋势,并提出了一些蠕虫防御的措施。  相似文献   

6.
在垃圾邮件最为猖獗的时候,占到当时互联网上所有电子邮件总数量的50%以上。网络钓鱼形式的垃圾邮件使各种欺诈大行网上,群发邮件所带蠕虫病毒阻塞网络,利用垃圾邮件传播淫秽、反动等有害信息……亿万网民苦不堪言。  相似文献   

7.
由于当今世界互联网的开放性和计算机软件的脆弱性,导致以电子邮件等作为病毒载体来传播的问题愈发严重,电子邮件病毒传播模型的研究对邮件病毒的抑制有指导性的作用.文中从三个不同角度提出三个不同的反映计算机邮件病毒传播方式的模型来进行分析研究,一个是SIR改进型的通用传播模型,然后是基于无尺度网络拓扑结构的SIR模型,最后是笔者提出的一个数学模型并对该模型的进行仿真试验,结果表明与理论分析相一致.通过这三个模型的介绍,可以制定相关的病毒阻隔策略,以利用最小的资源切断邮件病毒在网络上的传播链.  相似文献   

8.
邮件蠕虫利用e-mail在具有power-law结构特点的网络中进行传播,使得传统的蠕虫防御策略失效。结合power-law网络拓扑结构的特点,引入节点免疫和邮件服务器参与两种防御策略,分别对重复感染与非重复感染两种类型的邮件蠕虫传播进行了实验仿真。结果表明,节点的优先免疫类型、免疫起始时间、邮件服务器参与防御时间及蠕虫邮件识别正确率都与邮件蠕虫的传播有着紧密联系。  相似文献   

9.
对于僵尸网络传播特性的研究已有一定进展,无尺度网络传播模型更加符合实际网络特征,基于KSC算法对僵尸程序在无尺度网络中的传播特性进行研究,研究发现,模型基本能够体现僵尸程序在无尺度网络的传播特性和感染特征。  相似文献   

10.
欧阳晨星  谭良  朱贵琼 《计算机工程》2012,38(5):126-128,132
主流传播模型不能准确反映僵尸程序在Internet中的传播特性。针对该问题,提出一种基于无尺度网络结构的僵尸网络传播模型。该模型考虑了Internet网络的增长特性和择优连接特性,能够反映实际网络中的无尺度特性,更符合真实Internet网络中僵尸程序的传播规律和感染特性。  相似文献   

11.
垃圾邮件的处理是电子邮件服务中非常重要的功能,该文在对标准邮件集表示为向量空间模型,降维处理处理工作的基础上,运用神经网络集成的方法来构造邮件分类器,对邮件进行过滤;该方法在垃圾邮件语料库上进行了实验,实验证明该方法对于垃圾邮件的过滤有较好的效果。  相似文献   

12.
A note on the spread of worms in scale-free networks.   总被引:2,自引:0,他引:2  
This paper considers the spread of worms in computer networks using insights from epidemiology and percolation theory. We provide three new results. The first result refines previous work showing that epidemics occur in scale-free graphs more easily because of their structure. We argue, using recent results from random graph theory that for scaling factors between 0 and approximately 3.4875, any computer worm infection of a scale-free network will become an epidemic. Our second result uses this insight to provide a mathematical explanation for the empirical results of Chen and Carley, who demonstrate that the Countermeasure Competing strategy can be more effective for immunizing networks to viruses or worms than traditional approaches. Our third result uses random graph theory to contradict the current supposition that, for very large networks, monocultures are necessarily more susceptible than diverse networks to worm infections.  相似文献   

13.
Unsolicited or spam email has recently become a major threat that can negatively impact the usability of electronic mail. Spam substantially wastes time and money for business users and network administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium for distributing harmful code and/or offensive content. In this paper, we explore the application of the GMDH (Group Method of Data Handling) based inductive learning approach in detecting spam messages by automatically identifying content features that effectively distinguish spam from legitimate emails. We study the performance for various network model complexities using spambase, a publicly available benchmark dataset. Results reveal that classification accuracies of 91.7% can be achieved using only 10 out of the available 57 attributes, selected through abductive learning as the most effective feature subset (i.e. 82.5% data reduction). We also show how to improve classification performance using abductive network ensembles (committees) trained on different subsets of the training data. Comparison with other techniques such as neural networks and naïve Bayesian classifiers shows that the GMDH-based learning approach can provide better spam detection accuracy with false-positive rates as low as 4.3% and yet requires shorter training time.  相似文献   

14.
一个基于粗糙集理论的邮件分类模型   总被引:3,自引:1,他引:3  
论文讨论了垃圾邮件对网络造成的影响。大量未经收件人请求而发送的垃圾邮件充斥了用户的电子信箱,给用户造成带宽、时间和金钱的浪费。为此,论文提出一个基于粗糙集(RoughSet)的模型,并进行了实验分析,通过与流行的邮件分类模型朴素贝叶斯模型的比较,证明本文提出的基于粗糙集(RoughSet)的模型可以大大降低把正常邮件错划为垃圾邮件的比率。  相似文献   

15.
In this paper we present a detailed study of the behavioral characteristics of spammers based on a two-month email trace collected at a large US university campus network. We analyze the behavioral characteristics of spammers that are critical to spam control, including the distributions of message senders, spam and non-spam messages by spam ratios; the statistics of spam messages from different spammers; the spam arrival patterns across the IP address space; and the active duration of spammers, among others. In addition, we also formally confirm an informal observation that spammers may hijack network prefixes in sending spam messages, by correlating the arrivals of spam messages with the BGP route updates of the corresponding networks. In this paper we present the detailed results of the measurement study; in addition, we also discuss the implications of the findings for the (content-independent) anti-spam efforts.  相似文献   

16.
基于P2P协作的垃圾邮件发送行为识别技术研究   总被引:1,自引:0,他引:1       下载免费PDF全文
在分析目前垃圾邮件过滤技术的基础上,并根据垃圾邮件大量发送行为特征,提出了一种基于P2P协作的垃圾邮件发送行为识别技术。该技术将各邮件服务器组成一个反垃圾邮件(Anti-Spam)P2P网络,每个邮件服务器储存可疑邮件信息并将这些信息共享在Anti-Spam P2P网络上,然后根据可疑邮件信息在Anti-Spam P2P网络上进行协作识别垃圾邮件。实验结果表明,该技术是针对垃圾邮件的群发特征而不依赖于邮件内容、语言类型或格式分析,在MTA阶段就能过滤大量垃圾邮件,提高了处理速度和准确率并节省大量的系统资源,具有良好的过滤性能。  相似文献   

17.
Email spam filtering is typically treated as a binary classification problem that can be solved by machine learning algorithms. We argue that a three-way decision approach provides a more meaningful way to users for precautionary handling their incoming emails. Three email folders instead of two are produced in a three-way spam filtering system, a suspected folder is added to allow users make further examinations of suspicious emails, thereby reducing the chances of misclassification. Different from existing ternary email spam filtering systems, we focus on two issues that are less studied, that is, the computation of required thresholds to define the three email categories, and the interpretation of the cost-sensitive characteristics of spam filtering. Instead of supplying the thresholds based on intuitive understandings of the levels of tolerance for errors, we systematically calculate the thresholds based on decision-theoretic rough set model. A loss function is interpreted as the costs of making classification decisions. A decision is made for which the overall cost is minimum. Experimental results show that the new approach reduces the error rate of misclassifying a legitimate email to spam and demonstrates a better performance for the cost-sensitivity aspect.  相似文献   

18.
张建  严珂  马祥 《计算机应用》2022,42(3):770-777
垃圾信息的识别是自然语言处理方面主要的任务之一。传统方法是基于文本特征或词频的方法,其识别准确率主要依赖于特定关键词的出现与否,存在对关键词识别错误或对未出现关键词的垃圾信息文本识别能力较差的问题,提出基于神经网络的方法。首先,利用传统方法针对这一类垃圾信息文本进行识别训练和测试;然后,利用从垃圾短信、广告和垃圾邮件数据集中挑选出传统方法识别困难的垃圾信息,再从原数据集中随机挑选出同样数量的正常信息,将其组成三个无重复数据的新数据集;最后,以卷积神经网络和循环神经网络为基础,建立了三个模型,并在新数据集上进行识别训练。实验结果表明,基于神经网络的方法可以从文本中学习到更好的语义特征,在三个数据集上均能达到98%以上的准确率,高于朴素贝叶斯(NB)、随机森林(RF)、支持向量机(SVM)等传统方法。实验结果还显示,不同的神经网络适用于不同长度的文本分类,由循环神经网络组成的模型擅长识别句子长度的文本,由卷积神经网络组成的模型擅长识别段落长度的文本,由两者共同组成的模型擅长识别篇章长度的文本。  相似文献   

19.
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.  相似文献   

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