E-mail Spam Filtering Using Adaptive Genetic Algorithm

Shrivastava, Jitendra; Bindu, M. Hima

doi:10.5815/ijisa.2014.02.07

Cited by 18 publications

(8 citation statements)

References 16 publications

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“…The performance of the process relies on the genetic algorithm parameters and dataset. The suitability of the algorithm is more than 82% [9]. …”

Section: Email Spam Filtering Using Adaptive Genetic Algorithmmentioning

confidence: 94%

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A Survey of Email Service; Attacks, Security Methods and Protocols

Al-Mashhadi¹,

Alabiech²

2017

IJCA

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Email security becomes a sensitive case to study in the field of information security. From the most important security issues of network is how to secure Email from the threats that can be exposed of the security of massages by attackers. In addition, what are the techniques that can be using to secure the Email? Different solutions and several standards levels have been fashioned according to the last security requirements that enhance the Email security. Some of the current enhancements concentrate on keeping the exchange of data through Email in an integral and confident way. While the others concentrate on authenticating the sender and confirm that, client will not repudiate from his message. This paper will explain Email works and discuss different threats in Email Communication and several Email security solutions. This paper introduces several models and techniques utilized to fix and enhance the safety of Email systems.

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“…The performance of the process relies on the genetic algorithm parameters and dataset. The suitability of the algorithm is more than 82% [9]. …”

Section: Email Spam Filtering Using Adaptive Genetic Algorithmmentioning

confidence: 94%

“…Spam rosters sending are created always by stealing mailing list or by searching info from internet [3,9] and to overcome Email Spamming, Email filtering can be used depending on the Email content [10].…”

Section: Email Spammingmentioning

confidence: 99%

A Survey of Email Service; Attacks, Security Methods and Protocols

Al-Mashhadi¹,

Alabiech²

2017

IJCA

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“…If the rule is found in the message, then the weight of the rule is added to the final score of the email. At the end, if the final score is found to be exceeding some threshold value, then the email is declared as spam [12]. The drawback of Rule-Based Spam Detection Technique is that it is a set of rules that is very huge and static that causes less performance [6].…”

Section: ) Rule-based Filtersmentioning

confidence: 99%

A survey on spam detection techniques

Sharma¹,

sha²,

Manisha³

et al. 2014

International Journal of Advanced Research in Computer and Comm

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Today e-mails have become one of the most popular and economical forms of communication for Internet users. Thus due to its popularity, the e-mail is going to be misused. One such misuse is the posting of unwelcome, unwanted e-mails known as spam or junk e-mails [1]. E-mail spam has various consequences. It reduces productivity, takes extra space in mail boxes, extra time, extend software damaging viruses, and materials that contains potentially harmful information for Internet users, destroy stability of mail servers, and as a result users spend lots of time for sorting incoming mail and deleting unwanted correspondence. So there is a need of spam detection so that its consequences can be reduced [2]. In this paper, we present various spam detection techniques.

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“…In this algorithm, initial population is generated randomly. IN feature representation is considered as a chromosome, and if the value of chromosome [i] is 1, the ith feature is selected for classification, while if it is 0, then these features will be removed [19,20]. Figure 1 shows feature presentation as a chromosome.…”

Section: Initialize Populationmentioning

confidence: 99%

Spam filtering by using Genetic based Feature Selection

kalaibar¹,

Razavi²

2014

IJCATR

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Abstract:Spam is defined as redundant and unwanted electronical letters, and nowadays, it has created many problems in business life such as occupying networks bandwidth and the space of user's mailbox. Due to these problems, much research has been carried out in this regard by using classification technique. The resent research show that feature selection can have positive effect on the efficiency of machine learning algorithm. Most algorithms try to present a data model depending on certain detection of small set of features. Unrelated features in the process of making model result in weak estimation and more computations. In this research it has been tried to evaluate spam detection in legal electronica letters, and their effect on several Machin learning algorithms through presenting a feature selection method based on genetic algorithm. Bayesian network and KNN classifiers have been taken into account in classification phase and spam base dataset is used.

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E-mail Spam Filtering Using Adaptive Genetic Algorithm

Cited by 18 publications

References 16 publications

A Survey of Email Service; Attacks, Security Methods and Protocols

A Survey of Email Service; Attacks, Security Methods and Protocols

A survey on spam detection techniques

Spam filtering by using Genetic based Feature Selection

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