A survey and evaluation of supervised machine learning techniques for spam e-mail filtering

Vyas, Tarjani; Prajapati, Payal; Gadhwal, Somil

doi:10.1109/icecct.2015.7226077

Cited by 23 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Concerning SVM, another popular spam-classification algorithm, it was showed that SVMs are robust to both different datasets and pre-processing techniques [23]. Its superiority to NB, k-NN, decision trees, and MLP approaches was demonstrated is recent comparative studies [45,75,82]. AISs [76] represent another promising method for spam filtering.…”

Section: Spam Filtering Using Machine Learning: a Literature Reviewmentioning

confidence: 99%

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks

Barushka

Hájek

2018

Appl Intell

View full text Add to dashboard Cite

Rapid growth in the volume of unsolicited and unwanted messages has inspired the development of many anti-spam methods. Supervised anti-spam filters using machine-learning methods have been particularly effective in categorizing spam and non-spam messages. These automatically integrate spam corpora pre-processing, appropriate word lists selection, and the calculation of word weights, usually in a bag-of-words fashion. To develop an accurate spam filter is challenging because spammers attempt to decrease the probability of spam detection by using legitimate words. Complex models are therefore needed to solve such a problem. However, existing spam filtering methods usually converge to a poor local minimum, cannot effectively handle high-dimensional data and suffer from overfitting issues. To overcome these problems, we propose a novel spam filter integrating an N-gram tf.idf feature selection, modified distribution-based balancing algorithm and a regularized deep multi-layer perceptron NN model with rectified linear units (DBB-RDNN-ReL). As demonstrated on four benchmark spam datasets (Enron, SpamAssassin, SMS spam collection and Social networking), the proposed approach enables capturing more complex features from high-dimensional data by additional layers of neurons. Another advantage of this approach is that no additional dimensionality reduction is necessary and spam dataset imbalance is addressed using a modified distribution-based algorithm. We compare the performance of the approach with that of state-of-the-art spam filters (Minimum Description Length, Factorial Design using SVM and NB, Incremental Learning C4.5, and Random Forest, Voting and Convolutional Neural Network) and several machine learning algorithms commonly used to classify text. We show that the proposed model outperforms these other methods in terms of classification accuracy, with fewer false negatives and false positives. Notably, the proposed spam filter classifies both major (legitimate) and minor (spam) classes well on personalized / non-personalized and balanced / imbalanced spam datasets. In addition, we show that the proposed model performs better than the results reported by previous studies in terms of accuracy. * significantly higher (p=0.05).

show abstract

Section: Spam Filtering Using Machine Learning: a Literature Reviewmentioning

confidence: 99%

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks

Barushka

Hájek

2018

Appl Intell

View full text Add to dashboard Cite

show abstract

“…ey examined 35 well-known cyber dataset by dividing them into seven categories. ese categories include Internet traffic-based, network trafficbased, Interanet traffic-based, electrical network-based, virtual private network-based, andriod apps-based, IoT Vyas et al [22] present a review on supervised machine learning strategies for filtering spam emails. ey concluded that the Naïve Bayes method provides faster results and decent precision over all other methods (except SVM and ID3) from all the techniques discussed.…”

Section: Comparison With Previous Surveysmentioning

confidence: 99%

Machine Learning Techniques for Spam Detection in Email and IoT Platforms: Analysis and Research Challenges

Ahmed

Amin

Aldabbas

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

Nowaday, emails are used in almost every field, from business to education. Emails have two subcategories, i.e., ham and spam. Email spam, also called junk emails or unwanted emails, is a type of email that can be used to harm any user by wasting his/her time, computing resources, and stealing valuable information. The ratio of spam emails is increasing rapidly day by day. Spam detection and filtration are significant and enormous problems for email and IoT service providers nowadays. Among all the techniques developed for detecting and preventing spam, filtering email is one of the most essential and prominent approaches. Several machine learning and deep learning techniques have been used for this purpose, i.e., Naïve Bayes, decision trees, neural networks, and random forest. This paper surveys the machine learning techniques used for spam filtering techniques used in email and IoT platforms by classifying them into suitable categories. A comprehensive comparison of these techniques is also made based on accuracy, precision, recall, etc. In the end, comprehensive insights and future research directions are also discussed.

show abstract

“…Use Another illegal mail recognition method was suggested by Tarjani Vyas et al [9]. The goal is not only to identify mail as spam or ham, but also to minimize the risk of false positive and false negative.…”

Section: Literature Reviewmentioning

confidence: 99%

Spam Mail Scanning Using Machine Learning Algorithm

Bibi¹,

Latif²,

Khalid³

et al. 2020

JCP

View full text Add to dashboard Cite

Emails are used in professional and personal level as a way of communication. With the passage of time emails are used for advertisement, spreading virus and fraud email for plaguing users of the internet. These type of unsolicited emails are categorized as spam and other legitimated emails are categorized as ham. Over the year several machine learning algorithms are used to predict emails category. In this paper we reflect on the classifier which is good for text classification. We evaluate machine learning algorithm on spam emails detection and outcomes shows naï ve Bayes algorithm gives effective accuracy and precision using WEKA and our email management system which utilize php-ml library hosted by GitHub. Also comparative study with SVM and previous existing system in terms of accuracy and dataset used.

show abstract

A survey and evaluation of supervised machine learning techniques for spam e-mail filtering

Cited by 23 publications

References 5 publications

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks

Machine Learning Techniques for Spam Detection in Email and IoT Platforms: Analysis and Research Challenges

Spam Mail Scanning Using Machine Learning Algorithm

Contact Info

Product

Resources

About