Undoubtedly,spam is a serious problem,and the number of spam emails is increased rapidly.Besides,the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used...Undoubtedly,spam is a serious problem,and the number of spam emails is increased rapidly.Besides,the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used for spam filtering.Also,some emergent spam detection techniques use machine learning methods and feature extraction.Some methods and algorithms have been introduced for spam detecting and filtering.This research proposes two models for spam detection and feature selection.The first model is evaluated with the email spam classification dataset,which is based on reducing the number of keywords to its minimum.The results of this model are promising and highly acceptable.The second proposed model is based on creating features for spam detection as a first stage.Then,the number of features is reduced using three well-known metaheuristic algorithms at the second stage.The algorithms used in the second model are Artificial Bee Colony(ABC),Ant Colony Optimization(ACO),and Particle Swarm Optimization(PSO),and these three algorithms are adapted to fit the proposed model.Also,the authors give it the names AABC,AACO,and APSO,respectively.The dataset used for the evaluation of this model is Enron.Finally,well-known criteria are used for the evaluation purposes of this model,such as true positive,false positive,false negative,precision,recall,and F-Measure.The outcomes of the second proposed model are highly significant compared to the first one.展开更多
Spam or unsolicited emails constitute a major threat to the Internet, the corporations, and the end-users. Statistics show that about 70% - 80% of the emails are spam. There are several techniques that have been imple...Spam or unsolicited emails constitute a major threat to the Internet, the corporations, and the end-users. Statistics show that about 70% - 80% of the emails are spam. There are several techniques that have been implemented to react to the spam on its arrival. These techniques consist in filtering the emails and placing them in the Junk or Spam folders of the users. Regardless of the accuracy of these techniques, they are all passive. In other words, they are like someone is hitting you and you are trying by all the means to protect yourself from these hits without fighting your opponent. As we know the proverbs 'The best defense is a good offense' or 'Attack is the best form of defense'. Thus, we believe that attacking the spammers is the best way to minimize their impact. Spammers send millions of emails to the users for several reasons and usually they include some links or images that direct the user to some web pages or simply to track the users. The proposed idea of attacking the spammers is by building some software to collect these links from the Spam and Junk folders of the users. Then, the software periodically and actively visit these links and the subsequent redirect links as if a user clicks on these links or as if the user open the email containing the tracking link. If this software is used by millions of users (included in the major email providers), then this will act as a storm of Distributed Denial of Service attack on the spammers servers and there bandwidth will be completely consumed by this act. In this case, no human can visit their sites because they will be unavailable. In this paper, we describe this approach and show its effectiveness. In addition, we present an application we have developed that can be used for this reason.展开更多
为了有效接受邮件客户端反馈并自动根据邮件客户端反馈对邮件过滤系统做出调整,提出了用户反馈自适应的POP3邮件过滤代理模型SAMFUF(self-adaptive mail filtering POP3 proxy based on users’feedback).SAMFUF采用异步连接模拟同步连...为了有效接受邮件客户端反馈并自动根据邮件客户端反馈对邮件过滤系统做出调整,提出了用户反馈自适应的POP3邮件过滤代理模型SAMFUF(self-adaptive mail filtering POP3 proxy based on users’feedback).SAMFUF采用异步连接模拟同步连接方式建立邮件客户端和邮件服务器间POP3连接,实现了邮件客户端与邮件服务器间的透明性连接;将最大熵、贝叶斯和支持向量机等基于内容过滤的方法从邮件过滤系统中独立出来,实现了通用邮件过滤系统的设计;通过在邮件中插入包含邮件唯一标识、分类结果等信息的HTML表单的标签以及调整邮件过滤模型,实现了基于邮件客户端的用户反馈以及邮件过滤系统的自适应功能;采用线程池管理线程和overlapped I/O事件通信方式实现邮件客户端与邮件服务器间通信,实现了邮件过滤服务的高效率和稳定性.实验表明,SAMFUF在垃圾邮件过滤上具有较高的效率和准确率.展开更多
文摘Undoubtedly,spam is a serious problem,and the number of spam emails is increased rapidly.Besides,the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used for spam filtering.Also,some emergent spam detection techniques use machine learning methods and feature extraction.Some methods and algorithms have been introduced for spam detecting and filtering.This research proposes two models for spam detection and feature selection.The first model is evaluated with the email spam classification dataset,which is based on reducing the number of keywords to its minimum.The results of this model are promising and highly acceptable.The second proposed model is based on creating features for spam detection as a first stage.Then,the number of features is reduced using three well-known metaheuristic algorithms at the second stage.The algorithms used in the second model are Artificial Bee Colony(ABC),Ant Colony Optimization(ACO),and Particle Swarm Optimization(PSO),and these three algorithms are adapted to fit the proposed model.Also,the authors give it the names AABC,AACO,and APSO,respectively.The dataset used for the evaluation of this model is Enron.Finally,well-known criteria are used for the evaluation purposes of this model,such as true positive,false positive,false negative,precision,recall,and F-Measure.The outcomes of the second proposed model are highly significant compared to the first one.
文摘Spam or unsolicited emails constitute a major threat to the Internet, the corporations, and the end-users. Statistics show that about 70% - 80% of the emails are spam. There are several techniques that have been implemented to react to the spam on its arrival. These techniques consist in filtering the emails and placing them in the Junk or Spam folders of the users. Regardless of the accuracy of these techniques, they are all passive. In other words, they are like someone is hitting you and you are trying by all the means to protect yourself from these hits without fighting your opponent. As we know the proverbs 'The best defense is a good offense' or 'Attack is the best form of defense'. Thus, we believe that attacking the spammers is the best way to minimize their impact. Spammers send millions of emails to the users for several reasons and usually they include some links or images that direct the user to some web pages or simply to track the users. The proposed idea of attacking the spammers is by building some software to collect these links from the Spam and Junk folders of the users. Then, the software periodically and actively visit these links and the subsequent redirect links as if a user clicks on these links or as if the user open the email containing the tracking link. If this software is used by millions of users (included in the major email providers), then this will act as a storm of Distributed Denial of Service attack on the spammers servers and there bandwidth will be completely consumed by this act. In this case, no human can visit their sites because they will be unavailable. In this paper, we describe this approach and show its effectiveness. In addition, we present an application we have developed that can be used for this reason.
文摘为了有效接受邮件客户端反馈并自动根据邮件客户端反馈对邮件过滤系统做出调整,提出了用户反馈自适应的POP3邮件过滤代理模型SAMFUF(self-adaptive mail filtering POP3 proxy based on users’feedback).SAMFUF采用异步连接模拟同步连接方式建立邮件客户端和邮件服务器间POP3连接,实现了邮件客户端与邮件服务器间的透明性连接;将最大熵、贝叶斯和支持向量机等基于内容过滤的方法从邮件过滤系统中独立出来,实现了通用邮件过滤系统的设计;通过在邮件中插入包含邮件唯一标识、分类结果等信息的HTML表单的标签以及调整邮件过滤模型,实现了基于邮件客户端的用户反馈以及邮件过滤系统的自适应功能;采用线程池管理线程和overlapped I/O事件通信方式实现邮件客户端与邮件服务器间通信,实现了邮件过滤服务的高效率和稳定性.实验表明,SAMFUF在垃圾邮件过滤上具有较高的效率和准确率.
