Clustering for semi-supervised spam filtering

Whissell, John S.; Clarke, Charles L. A.

doi:10.1145/2030376.2030391

Cited by 16 publications

(9 citation statements)

References 19 publications

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“…By contrast, their approach could leverage small amount of labeled data and large amount of unlabeled data for identifying spams and training a classification model simultaneously. Later, Whissell and Clarke [60] considered a specific scenario for semi-supervised spam filtration: that is, when a large amount of training data is available, but only a few true labels can be obtained for that data. They thus presented two spam filtering approaches for such scenario, both starting with a cluster of training emails.…”

Section: Related Workmentioning

confidence: 99%

Design of multi-view based email classification for IoT systems via semi-supervised learning

Meng

Tan

et al. 2019

Journal of Network and Computer Applications

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Section: Related Workmentioning

confidence: 99%

Design of multi-view based email classification for IoT systems via semi-supervised learning

Meng

Tan

et al. 2019

Journal of Network and Computer Applications

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“…In spam filtering, there has been research regarding using clustering as a way to detect spam. Some research suggests that the ham and spam emails can be divided into clusters using semi supervised clustering method [10]. But most of the conventional clustering methods have some limitations.…”

Section: Clusteringmentioning

confidence: 99%

Spam, a Digital Pollution and Ways to Eradicate It

Reddy,

Chaudhary,

Kandasamy

2019

IJEAT

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Due to the growing popularity of the microblogging and networking sites like twitter, Gmail, Facebook etc., there has been an increase in the number of spammers. Spammers on Twitter seem to be more dangerous than the mail spammers as they exploit the limitation on the characters of Twitter for their own purposes. Spammers have also become creative in framing their content to cleverly escape the classifiers. This survey is thus mainly used to discuss and analyze the recent research that had been put forth regarding the spam detection in social media sites such as Twitter. This survey analyses the papers that tackled various problems faced on Twitter and the problems faced by the methods that have already been presented before. We then compared all the methods present in the papers to see which method or combination of methods could give the best result in detecting spam.

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“…Whistel and Clarke partition e-mail datasets into ham and spam clusters. The research focused on a novel investigation of email spam clustering [16]. The study showed significant result by using clustering approach and give better result than semi-supervised approaches.…”

Section: Clustering Techniquesmentioning

confidence: 99%

Review Study of Hoax Email Characteristic

Yuliani¹,

Sahib²,

Abdollah³

et al. 2018

IJET

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Hoax on email is one form of attack in the cyber world where an email account will be sent with fake news that has many goals to take advantage or raise the rating of sales of a product. A Hoax can affect many people by damaging the credibility of the image of a person or group. The phenomenon of this hoax would cause anxiety in the community and even more bad effects because of the potential for the wrong power of the news or information. In this paper we review the Hoax detection systems, Types of Hoax, and machine learning models that has been used to detect the Hoax. This work serves as a basis for further studies on Hoax detection systems.

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Clustering for semi-supervised spam filtering

Cited by 16 publications

References 19 publications

Design of multi-view based email classification for IoT systems via semi-supervised learning

Design of multi-view based email classification for IoT systems via semi-supervised learning

Spam, a Digital Pollution and Ways to Eradicate It

Review Study of Hoax Email Characteristic

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