A novel feature extraction approach in SMS spam filtering for mobile communication: one‐dimensional ternary patterns

Kaya, Yılmaz; Ertuğrul, Ömer Faruk

doi:10.1002/sec.1660

Cited by 16 publications

(8 citation statements)

References 27 publications

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“…Some of the previous work based on the content are: Jali (2016) carried out an analysis of the ability to control features, analyzing information, and affect circumstances in the classification of SMS spam messages [7]. Kaya and Ertuğrul (2016) have implemented a method based on local ternary patterns to extract two distinct features from SMS messages and many machine learning approaches have been applied to distinguish SMS spam [8]. An approach that can detect spam messages using 10 features and 5 machine learning algorithms namely: naive Bayes, logistic regression, J48, decision table, and random forest was proposed by Choudhary and Jain (2017).…”

Section: Related Workmentioning

confidence: 99%

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

Hameed¹,

Ali²

2021

IJIES

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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.

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Section: Related Workmentioning

confidence: 99%

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

Hameed¹,

Ali²

2021

IJIES

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“…Kaya and Ertuğrul introduced a method based on local ternary patterns to extract two distinct features set low and up features from SMS messages and several machine learning methods were applied for classifying SMS spam. The evaluation results over three separate SMS datasets gained accuracy 93.318%, 87.15%, and 94.10% [11].…”

Section: Introductionmentioning

confidence: 96%

Differential evolution detection models for SMS spam

Hameed

2021

IJECE

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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.

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“…In recent years, some researchers have shown an increasing interest in applying information theory, symbolic dynamic theory, variants of the texture analysis technique and deep learning to fault diagnosis [15]- [17]. The application of these novel methods overcomes the shortcomings of traditional methods to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Pei

2021

Measurement Science Review

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Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

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A novel feature extraction approach in SMS spam filtering for mobile communication: one‐dimensional ternary patterns

Cited by 16 publications

References 27 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

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

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