Convolutional Neural Network Based SMS Spam Detection

Popovac, Milivoje; Karanovic, Mirjana; Sladojević, Srdjan; Arsenović, Marko; Anderla, Andraš

doi:10.1109/telfor.2018.8611916

Cited by 34 publications

(18 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Table 3 and Fig. 10 provide a contrast between RMDL's accuracy and the best accuracy in the four deep learning articles [8][9][10][11] listed in the related work section. Using complicated 3CNN architecture in [8], the best accuracy was achieved.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In addition, some research has been presented in [8][9][10][11] on deep learning approaches for the detection of SMS spam. Using text information only, CNN and LSTM were tested in [8].…”

Section: Related Workmentioning

confidence: 99%

“…SLTSM uses the semantic layer on top of the LSTM. The semantic layer is built using ConceptNet, WordNet, and Word2Vec.In [10] preprocessing of dataset was applied through stemming, sentiment analysis, stop word removing and tokenization. To be an input to CNN, a matrix of TF-IDF features was created.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

The Impact of Deep Learning Techniques on SMS Spam Filtering

Gomaa¹

2020

IJACSA

View full text Add to dashboard Cite

Over the past decade, phone calls and bulk SMS have been fashionable. Although many advertisers assume that SMS has died, it is still alive. It is one of the simplest and most cost-effective marketing tools for companies to communicate on a personal level to their customers. The spread of SMS has led to the risk of spam. Most of the previous studies that attempted to detect spam were based on manually extracted features using classical machine learning classifiers. This paper explores the impact of applying various deep learning techniques on SMS spam filtering; by comparing the results of seven different deep neural network architectures and six classifiers for classical machine learning. Proposed methodologies are based on the automatic extraction of the required features. On a benchmark data set consisting of 5574 records, a fabulous accuracy of 99.26% has been resulted using Random Multimodel Deep Learning (RMDL) architecture.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

“…In addition, some research has been presented in [8][9][10][11] on deep learning approaches for the detection of SMS spam. Using text information only, CNN and LSTM were tested in [8].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Deep Learning Techniques on SMS Spam Filtering

Gomaa¹

2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…Of late, researchers have started to use deep neural networks such as CNN (Popovac et al, 2018), and LSTM model (Barushka & Hajek, 2018;Jain et al, 2019) features and 200 LSTM nodes, their model achieved an accuracy of 99.01%. They also used three different word embedding techniques: i) Word2Vec, ii) WordNet, and iii) ConcepNet.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning to filter SMS Spam

Roy

Singh

Banerjee

2020

Future Generation Computer Systems

149

View full text Add to dashboard Cite

The popularity of short message service (SMS) has been growing over the last decade. For businesses, these text messages are more effective than even emails. This is because while 98% of mobile users read their SMS by the end of the day, about 80% of the emails remain unopened. The popularity of SMS has also given rise to SMS Spam, which refers to any irrelevant text messages delivered using mobile networks. They are severely annoying to users. Most existing research that has attempted to filter SMS Spam has relied on manually identified features. Extending the current literature, this paper uses deep learning to classify Spam and Not-Spam text messages. Specifically, Convolutional Neural Network and Long Short-term memory models were employed. The proposed models were based on text data only, and self-extracted the feature set. On a benchmark dataset consisting of 747 Spam and 4,827 Not-Spam text messages, a remarkable accuracy of 99.44% was achieved.

show abstract

“…Traditionally, convolutional neural network has been utilized for problems related to classifying image. The paper by Milivoje et al [7] in 2018 ran counter to this idea by using CNN for classifying SMS spam messages. Crucial step in their work was preprocessing the data by removing stop words, tokenization, reducing text to lower case where they are working on 'SMS Spam Collection V.1' dataset.…”

Section: Application Of Detecting Spam In Smsmentioning

confidence: 99%

A survey of Emerging Techniques in Detecting SMS Spam

Alqahtani

Alghazzawi

2019

TMLAI

View full text Add to dashboard Cite

In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.

show abstract

Convolutional Neural Network Based SMS Spam Detection

Cited by 34 publications

References 11 publications

The Impact of Deep Learning Techniques on SMS Spam Filtering

The Impact of Deep Learning Techniques on SMS Spam Filtering

Deep learning to filter SMS Spam

A survey of Emerging Techniques in Detecting SMS Spam

Contact Info

Product

Resources

About