Graph Centrality Based Spam SMS Detection

Ishtiaq, Asra; Islam, Muhammad Arshad; Iqbal, Muhammad Azhar; Aleem, Muhammad; Ahmed, Usman

doi:10.1109/ibcast.2019.8667174

Cited by 12 publications

(9 citation statements)

References 5 publications

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“…Using parallelization results in reduced training time of the model. Extreme Gradient Boosting 42 is used for the classification and regression tasks, such as spam detection, face recognition, and financial predictions 42–47 …”

Section: Resultsmentioning

confidence: 99%

RALB‐HC: A resource‐aware load balancer for heterogeneous cluster

Ahmed

Aleem

Khalid

et al. 2019

Concurrency and Computation

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Summary In the heterogeneous computing environment, programmers map the applications either on CPUs or GPUs. However, this default mapping process does not produce improved results, particularly on the heterogeneous clusters. If one resource of the cluster is more compute capable, then most of the scheduling schemes favor that powerful device. In this scenario, the scheduling schemes overload the powerful resources while making all other compute resources remain under utilized. This load imbalance problem results in higher energy consumption and increased execution time. In this research, a novel Resource‐Aware Load Balancer for the Heterogeneous Cluster (RALB‐HC) is proposed that distributes workload based on resources computing capabilities and applications computing needs. The RALB‐HC uses supervised machine learning approach to classify applications using the static code‐features. The RALB‐HC framework comprises of two phases: (1) job mapping based on the availability of the resources and (2) the resource‐aware load balancing to achieve the higher resource utilization ratio. The experimental results on a large set of real‐world and synthetic workloads show that the RALB‐HC reduces execution time by 31.61%, increased resource utilization ratio by 67.8% and improved throughout 147.35% as compared to baseline scheduling schemes.

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Section: Resultsmentioning

confidence: 99%

RALB‐HC: A resource‐aware load balancer for heterogeneous cluster

Ahmed

Aleem

Khalid

et al. 2019

Concurrency and Computation

Self Cite

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“…In graph theory, centrality measurement identifies how important nodes are close to each other. Centrality measurement can be used to distinguish important nodes in the graph [1].…”

Section: Introductionmentioning

confidence: 99%

“…The centrality type that used on the research is degree centrality and closeness centrality. The result in the study showed that degree centrality gave the best precision and recall results with 81% and 76%, respectively [1]. In another research about implementing term weighting for text categorization, centrality is used to categorize a single label text that was implemented using term weighting (TW), which represents the term value of each centrality type.…”

Section: Introductionmentioning

confidence: 99%

Classifying Quranic Verse Topics using Word Centrality Measure

Yulianto

2021

RESTI

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Muslims believe that, as the speech of Allah, The Quran is a miracle that has specialties in itself. Some of the specialties that have studied are the regularities in the number of letters, words, vocabularies, etc. In the past, the early Islamic scholars identify these regularities manually, i.e. by counting the occurrence of each vocabulary by hand. This research tackles this problem by utilizing centrality in quranic verse topic classification. The goal of this research is to analyze the effect of The Quran word centrality measure on the topic classification of The Quran verses. To achieve this objective, the method of this research is constructing the Quran word graph, then the score of centralities included as one of the features in the verse topic classification. The effect of centrality is observed along with support vector machine (SVM) and naïve Bayes classifiers by performing two scenarios (with stopword and without stopword removal). The result shows that according to the centrality measure the word “الله” (Allah) is the most central in The Quran. The performance evaluation of the classification models shows that the use of centrality improves the hamming loss score from 0.43 to 0.21 on naïve Bayes classifier with stopword removal. Finally, both of classification method has a better performance in word graph that use stopword removal.

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“…Other interesting studies include the application of graph centrality in SMS spam detection, 56,57 factorial analysis filtering approaches on linguistic techniques, 14 the Rough-set and Naïve-Bayes algorithm, 58 FIMESS approach which uses external features like invalid characters, time inaccuracies and blacklisted keywords to filter SMS spam was presented in Androulidakis et al 59 After a thorough review of related studies, some of the identified shortfalls include the following: insufficient dataset for training effectively and classifying SMS, the problem of class imbalance, challenges with time complexity of some existing approaches, problem of increasing misclassification rate and so forth. Therefore, this study adopted a deep learning model based on BiLSTM for SMS spam classification using two SMS datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

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

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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.

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Graph Centrality Based Spam SMS Detection

Cited by 12 publications

References 5 publications

RALB‐HC: A resource‐aware load balancer for heterogeneous cluster

RALB‐HC: A resource‐aware load balancer for heterogeneous cluster

Classifying Quranic Verse Topics using Word Centrality Measure

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

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