Spam Detection in SMS Based on Feature Selection Techniques

Sharaff, Aakanksha

doi:10.1007/978-981-13-1498-8_49

Cited by 9 publications

(2 citation statements)

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“…In Ref. [ 23 ], the author has made a model considering feature selection techniques like the chi-square attribute selection technique and the information gain method to select the best features for the classification of SMS messages. The feature selection techniques used in this work are limited to the chi-square attribute selection technique and the information gain method, and the research is focused on SMS spam detection only; other types of text messages like reviews have not been considered for analysis.…”

Section: Related Workmentioning

confidence: 99%

AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis

Tripathy

Sharaff

2023

J Supercomput

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Sentiment analysis involves extricating and interpreting people’s views, feelings, beliefs, etc., about diverse actualities such as services, goods, and topics. People intend to investigate the users’ opinions on the online platform to achieve better performance. Regardless, the high-dimensional feature set in an online review study affects the interpretation of classification. Several studies have implemented different feature selection techniques; however, getting a high accuracy with a very minimal number of features is yet to be accomplished. This paper develops an effective hybrid approach based on an enhanced genetic algorithm (GA) and analysis of variance (ANOVA) to achieve this purpose. To beat the local minima convergence problem, this paper uses a unique two-phase crossover and impressive selection approach, gaining high exploration and fast convergence of the model. The use of ANOVA drastically reduces the feature size to minimize the computational burden of the model. Experiments are performed to estimate the algorithm performance using different conventional classifiers and algorithms like GA, Particle Swarm Optimization (PSO), Recursive Feature Elimination (RFE), Random Forest, ExtraTree, AdaBoost, GradientBoost, and XGBoost. The proposed novel approach gives impressive results using the Amazon Review dataset with an accuracy of 78.60 %, F1 score of 79.38 %, and an average precision of 0.87, and the Restaurant Customer Review dataset with an accuracy of 77.70 %, F1 score of 78.24 %, and average precision of 0.89 as compared to other existing algorithms. The result shows that the proposed model outperforms other algorithms with nearly 45 and 42% fewer features for the Amazon Review and Restaurant Customer Review datasets.

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Section: Related Workmentioning

confidence: 99%

AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis

Tripathy

Sharaff

2023

J Supercomput

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“…Saidani et al (2020) proposed a two‐level semantic analysis of emails in which the emails are first categorized into different domains and then the spam emails are detected in each specific domain using semantic features extracted manually and automatically. Sharaff (2019) has developed a SMS spam filtering mechanism by using the relevant features and these features were identified using feature selection techniques like information gain, Chi‐square, and so forth.…”

Section: Related Workmentioning

confidence: 99%

Deep learning‐based smishing message identification using regular expression feature generation

2022

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The increase in the number of undesired SMS termed smishing message and the data imbalance problem has generated a great demand for the development of more reliable anti-spam filters. State of the art machine learning approaches are being employed to recognize and separate spam messages. Most recent studies target message classification by using numerous properties and features of the words but fail to consider the circumstantial features like long-range dependencies between the words that are extremely important in identifying smishing messages. The idea is to develop an intelligent model that will distinguish between smishing messages and ham messages, by adopting a combined approach of regular expression (Regex), machine learning (ML) and deep learning (DL) models. Regex rules are generated using the dataset's spam messages for the purpose of refining the dataset. Support vector machine (SVM), Multinomial Naive Bayes and Random Forest are included under machine learning models and long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), stacked LSTM and stacked Bi-LSTM are included under deep learning models. The comparison between machine learning models and deep learning models is also carried out based on the performance evaluation parameters namely accuracy, precision, recall and F1 score of the models. It is observed that deep learning models perform better than machine learning models and the introduction of a regular expression to the dataset increases the efficiency of both the deep learning models and machine learning models.

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