SMS Spam Message Detection using Term Frequency-Inverse Document Frequency and Random Forest Algorithm

Sjarif, Nilam Nur Amir; Azmi, Nurulhuda Firdaus Mohd; Chuprat, Suriayati; Sarkan, Haslina Md; Yahya, Yazriwati; Sam, Suriani Mohd

doi:10.1016/j.procs.2019.11.150

Cited by 68 publications

(27 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The importance of other features is as follows message length, word numbers, upper case word, word less than three and alphanumeric characters. As shown in Table 3, the proposed model achieves the highest performance and outperforms the works presented in [1,[13][14] especially in terms of accuracy, which is the most important factor in measuring the performance of the classification. The superior performance of the proposed model is due to the inclusion of the new feature namely thematic SMS spam.…”

Section: Feature Results Analysismentioning

confidence: 85%

“…Sjarif (2019) introduced a new method based on computing TF-IDF and many classifier algorithms. The best result was obtained by Random forest had 97.5% accuracy [13]. Kumar (2020) presented a method based on selecting eleven features from the dataset, submitted to a set of classification algorithms to classify them as spam or ham.…”

Section: Related Workmentioning

confidence: 99%

“…The models in [1,13,14], which are adopted for comparison with the proposed model are based on the extraction of different features and applying many classification algorithms. The random forest algorithm gives the best results for all.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

Hameed¹,

Ali²

2021

IJIES

View full text Add to dashboard Cite

Over the last decade, the usage of short message services (SMS) as one of the vital communication services on mobile devices has grown. The growth of using this service has correspondingly increased the number of attacks on mobile devices such as SMS Spam. SMS spam is a concern to telecommunications service providers as they annoy the subscribers and cause them to loose commercial. Most current researches have attempted to detect SMS spam using different classifiers. In this paper, we propose a new method that focuses on binary particle swarm optimizationbased fuzzy rules selection for detecting SMS spam messages. First, we extract significant features from the SMS spam dataset. Then, a set of fuzzy rules based on the extracted features is generated. Finally, a binary particle swarm is suggested for picking the more powerful fuzzy rules that reduce the complexity and improve the model's performance. The SMS Spam benchmark dataset was used in the experiment. The attained results of the proposed model provide a recall of 98.8%, precision of 90.8%, F-measure of 94.6% and accuracy of 98.5% that indicate the proposed model can be a promising for detecting SMS spam.

show abstract

Section: Feature Results Analysismentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

Hameed¹,

Ali²

2021

IJIES

View full text Add to dashboard Cite

show abstract

“…After evaluation the individuals, DE selection operation is applied as shown in (7), the resultant vector from this operation is the vector with the higher objective function that will be passed to the next generation. [24] and false positive rate ( ) [25] are used for assessing the proposed SMS spam.…”

Section: Evaluation De Selection Operationmentioning

confidence: 99%

“…Among the approaches developed to combat the SMS spam is a classification of messages into SMS spam and ham. The challenge is that the messages are short and contains few words and these words may be abbreviated [6,7].…”

Section: Introductionmentioning

confidence: 99%

Differential evolution detection models for SMS spam

Hameed

2021

IJECE

View full text Add to dashboard Cite

With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative rate. Moreover, it surpasses the baseline methods.

show abstract

A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

Abayomi‐Alli

Misra

Abayomi‐Alli

2022

Concurrency and Computation

View full text Add to dashboard Cite

SMS, one of the most popular and fast‐growing GSM value‐added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F‐measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine‐tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state‐of‐the‐art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F‐measure.

show abstract

SMS Spam Message Detection using Term Frequency-Inverse Document Frequency and Random Forest Algorithm

Cited by 68 publications

References 8 publications

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

Differential evolution detection models for SMS spam

A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

Contact Info

Product

Resources

About