A support vector machine based naive Bayes algorithm for spam filtering

Feng, Wei; Sun, Jianguo; Zhang, Liguo; Cao, Cuiling; Yang, Qing

doi:10.1109/pccc.2016.7820655

Cited by 66 publications

(32 citation statements)

References 6 publications

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“…Feng et al [44] combined support machine vector and Naive Bayes to develop a spam filtering system. The proposed system was evaluated by the DATAMALL dataset and obtained a great spam-detection accuracy.…”

Section: Phishing and Spam Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.

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Section: Phishing and Spam Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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“…The order models are intended by machine learning in request algorithm. [2] Those you quit offering on that one machine learning in algorithm is Naïve bayesian classifier which is utilized for [1] with separate those spam and non-spam mails. Enormous information breaking down which is also framework to spam identification.…”

Section: Literature Surveymentioning

confidence: 99%

“…[4] Those research worth of effort need additionally conveyed out for expansion those correctness also the long haul effectiveness for framework. Sunil B. Rathod and Tareek M. Pattewar [1] [2] have used Bayesian classifier in order to separate legitimate message from spam messages. In paper [1] content of the email message is considered for implementation and works solely using Bayesian classifier.…”

Section: Literature Surveymentioning

confidence: 99%

“…The performance of the system is assessed in terms of accuracy, error, recall, time and precision. In paper [2], a combinational approach is used to filter both spam mail and malicious URLs. Bayesian classifier is used to filter spam messages and Decision tree is used to prevent malicious URLs from attacking the users.…”

Section: Literature Surveymentioning

confidence: 99%

“…Naïve Bayes Classifier Algorithm This classifier is a statistical classifier that will classify the email messages into spam or ham efficiently. It follows Bayesian team that employs the mathematical probability calculation for classification [2]. P (spam/word) = [P (word/spam) * P (spam)] / P (word) Naïve Bayes Classifier takes a whole message and uses keywords for recognizing the ham and spam email messages.…”

Section: Support Vector Machine Learning Algorithm Supportmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Email Spam Detection using Support Vector Machine

Prasanthi*¹,

Deepika²,

Anudeep³

et al. 2019

IJITEE

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This research paper proposes the electronics mail is small known as E-mail is used for communication between the people to person. E mail is providing as an necessary contribution for messaging by internet. Spam e mails are the unwanted messages that arise in high volume and are used by spammers for revealing users personal credentials. These e mails are regularly some sort of company/control announcement or viruses that the user receive without any notification. So as to defeat it, there need aid exactly existing frameworks that still don't keep them from striking. Therefore, there is a require should manufacture and proficient framework that adequately detects and more keeps the spam messages In those server utilizing the Naïve bayes classifier. Naïve bayes classifier is a mainstream statistical classifier utilized fundamentally for content arrangement

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