A novel machine learning and face recognition technique for fake accounts detection system on cyber social networks

Mughaid, Ala; Obeidat, Ibrahim; AlZu’bi, Shadi; Elsoud, Esraa Abu; Alnajjar, Asma; Alsoud, Anas Ratib; Abualigah, Laith

doi:10.1007/s11042-023-14347-8

Cited by 15 publications

(9 citation statements)

References 54 publications

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“…Furthermore, despite the higher accuracy of 99.30% for the method proposed in Elyusufi et al [9], important details such as the feature selection process are not included in the study. Also, the models presented by Mughaid et al [12] and Akyon and Kalfaoglu [14] are limited in sample size and lack detailed analysis with large datasets. Further, the model in Sahoo and Gupta [19] achieved a 99.52%…”

Section: Results Analysismentioning

confidence: 99%

“…It uses six base classifiers and cost-sensitive learning, enhancing detection rates on imbalanced datasets which serves as a base for the proposed work [5]. A study suggested digital face-processing authentication as a double-factor authentication method for OSN, with deep learning classification achieving 95% accuracy and SVM achieving 97.8% for fake profile detection [12]. The summary of significant existing studies for fake profile detection, along with the obtained results and their limitations, is presented in Table 1.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, in the proposed work on detecting fake profiles in OSN, seven base classifiers that are frequently used in the existing research on fake profile detection and found to be effective are chosen. These classifiers include DT [9], AdaBoost [16], KNN [10], LR [11], RF [8], NB [12], XGBoost [13], and SVM [14]. Each of these classifiers has its strengths in resolving classification problems, especially in fake profile detection.…”

Section: Base Learning Phase: Cost-sensitive Base Classifiersmentioning

confidence: 99%

“…These approaches encompass a wide variety of methodologies, including machine learning (ML) [6], data mining (DM), natural language processing (NLP) [7], and behavioral analysis [8], among others. Some of the common ML classifiers are used in the existing works, such as decision trees (DTs) [9], K-nearest neighbors (KNN) [10], logistic regression (LR) [11], random forest (RF) [8], naive Bayes (NB) [12], extreme gradient boosting (XGBoost) [13], and support vector machines (SVMs) [14] for classifying profile data in OSNs. While existing techniques for detecting fake profiles have contributed significantly to the literature, further work is needed to design robust and diverse models capable of successfully addressing the difficulties, especially those introduced by unbalanced datasets [15].…”

Section: Introductionmentioning

confidence: 99%

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A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Swetha Chikkasabbenahalli Venkatesh,

Sibi Shaji,

Balasubramanian Meenakshi Sundaram

2024

Proc. eng. technol. innov.

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Fake profile identification on social media platforms is essential for preserving a reliable online community. Previous studies have primarily used conventional classifiers for fake account identification on social networking sites, neglecting feature selection and class balancing to enhance performance. This study introduces a novel multistage stacked ensemble classification model to enhance fake profile detection accuracy, especially in imbalanced datasets. The model comprises three phases: feature selection, base learning, and meta-learning for classification. The novelty of the work lies in utilizing chi-squared feature-class association-based feature selection, combining stacked ensemble and cost-sensitive learning. The research findings indicate that the proposed model significantly enhances fake profile detection efficiency. Employing cost-sensitive learning enhances accuracy on the Facebook, Instagram, and Twitter spam datasets with 95%, 98.20%, and 81% precision, outperforming conventional and advanced classifiers. It is demonstrated that the proposed model has the potential to enhance the security and reliability of online social networks, compared with existing models.

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Section: Results Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Base Learning Phase: Cost-sensitive Base Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Swetha Chikkasabbenahalli Venkatesh,

Sibi Shaji,

Balasubramanian Meenakshi Sundaram

2024

Proc. eng. technol. innov.

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“…Instead of depending on programming, its algorithm is learnt from a big volume of data [ 24 ]. Machine learning has been used for computer vision [ 25 ], face recognition [ 26 ], autonomous driving [ 27 , 28 ], auxiliary decision making [ 29 , 30 ], brain–machine interface [ 31 ], cancer diagnosis and assessment [ 32 ], and chess game [ 33 ]. It includes supervised learning, unsupervised learning, and reinforcement learning [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

Rong,

Xu,

Sawan

2023

Biosensors

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We describe a machine learning (ML) approach to processing the signals collected from a COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector machine (SVM) were used to process both the raw data and the feature engineering data, and high performance for the qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/mL was achieved. Valid detection experiments contained 486 negative and 108 positive samples, and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contained 36 negative samples and 732 positive samples. The data distribution patterns of the valid and control detection dataset, based on T-distributed stochastic neighbor embedding (t-SNE), were used to study the distinguishability between positive and negative samples and explain the ML prediction performance. This work demonstrates that ML can be a generalized effective approach to process the signals and the datasets of biosensors dependent on resonant modes as biosensing mechanism.

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Disinformation detection using graph neural networks: a survey

Lakzaei,

Haghir Chehreghani,

Bagheri

2024

Artif Intell Rev

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The creation and propagation of disinformation on social media is a growing concern. The widespread dissemination of disinformation can have destructive effects on people’s attitudes and behavior. So, it is essential to detect disinformation as soon as possible. Therefore, the interest in effective detection techniques has grown rapidly in recent years. Major social media and social networking sites are trying to develop robust strategies to detect disinformation and prevent its spread. Machine learning techniques and especially neural networks, have an essential role in this task. In this paper, we review different approaches for automatic disinformation detection, with a focus on methods that leverage graph neural networks (GNNs). GNNs are very suitable tools for detecting disinformation in social networks. Because on the one hand, graphs are the most comprehensive way to model social networks and on the other hand, GNNs are the best tool for processing graph data. We define different forms of disinformation, and examine the features used and the methods presented from different perspectives. We also discuss relevant research areas, open problems, and future research directions for disinformation detection in social media.

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A novel machine learning and face recognition technique for fake accounts detection system on cyber social networks

Cited by 15 publications

References 54 publications

A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

Disinformation detection using graph neural networks: a survey

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