An effective multi-layered defense framework against spam

Zhou, Jianying; Chin, Wee-Yung; Román, Rodrigo; López, Javier

doi:10.1016/j.istr.2007.05.007

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…The code loads the data, defines the features and target variable, create a linear regression model, fits the model to each dataset, predicts the temperature for each dataset, calculate the error term and spam or not spam classification for each dataset, and saves the results to a CSV file. Finally, it evaluates the accuracy of the model using the accuracy_score function from scikit_learn [12].…”

Section: Resultsmentioning

confidence: 99%

Smart Spam Detection and Correction for Temperature in Heater

Ms. Dhanashri Ashok Patil,

Dr. Jayashree S Awati

2024

Int Res J Adv Engg Hub

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The efficient operation of heating systems relies heavily on accurate temperature monitoring. However, such systems are vulnerable to data corruption, which can lead to erroneous temperature readings and potentially hazardous conditions. We propose a novel approach to address this challenge by employing machine learning techniques for spam detection and correction in heater temperature data for the detection phase. We explore various machine learning algorithms including spam detection and classification models, to identify spam temperature readings. These models are trained on the preprocessed dataset and evaluated using appropriate metrics to assess their performance.

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Section: Resultsmentioning

confidence: 99%

Smart Spam Detection and Correction for Temperature in Heater

Ms. Dhanashri Ashok Patil,

Dr. Jayashree S Awati

2024

Int Res J Adv Engg Hub

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“…Several scholars have already made efforts to combat spammers. In [6], the authors define a multi-layer spam detection system. However it also extended to anti-spam initiatives on both the server and the client side.…”

Section: Methods and Methdologymentioning

confidence: 99%

A Machine Learning Based Email Spam Classification Framework Model: Related Challenges and Issues

Mallampati*¹,

Hegde²

2020

IJITEE

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Spam emails, also known as non-self, are unsolicited commercial emails or fraudulent emails sent to a particular individual or company, or to a group of individuals. Machine learning algorithms in the area of spam filtering is commonly used. There has been a lot of effort to render spam filtering more efficient in classifying e-mails as either ham (valid messages) or spam (unwanted messages) through the ML classifiers. We may recognize the distinguishing features of the material of documents. Much important work has been carried out in the area of spam filtering which cannot be adapted to various conditions and problems which are limited to certain domains. Our analysis contrasts the positives methods as well as some shortcomings of current ML methods and open spam filters study challenges. We suggest some of the new ongoing approaches towards deep leaning as potential tactics that can tackle the challenge of spam emails efficiently.

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“…However, our proposed model is different from these salient works. Jianying et al [6] describe a multi layer framework for spam detection. They divide the spam detection techniques between server and client side deployments.…”

Section: Related Workmentioning

confidence: 99%