Spam Detection using ANN and ABC Algorithm

Singh, Amar; Chahal, Nidhi; Singh, Simranjit; Gupta, Suneet Kumar

doi:10.1109/confluence51648.2021.9377061

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…On the e-mail spam dataset, Naive Bayes and Support Vector Machine achieved the highest accuracy of over 90%. The importance of machine learning techniques for spam text classification is studied by Al-Zoubi et al (2018) , Singh et al (2021) , Tang, Qian & You (2020) in their work in which they conclude that Machine Learning techniques overcome the drawbacks of rule-based techniques for spam content detection.…”

Section: Spam Text Classification Techniquesmentioning

confidence: 99%

A systematic literature review on spam content detection and classification

Kaddoura

Chandrasekaran²,

Popescu

et al. 2022

PeerJ Computer Science

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The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection.

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Section: Spam Text Classification Techniquesmentioning

confidence: 99%

A systematic literature review on spam content detection and classification

Kaddoura

Chandrasekaran²,

Popescu

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…The research conclusion is that, in terms of classification accuracy, the hybrid model outperforms other spam detection techniques. One of the models for spam detection systems in social networks utilizing artificial neural network machine learning techniques enhanced with the artificial bee colony optimization algorithm is presented in [69]. In [70], the community-inspired firefly algorithm for spam detection is proposed for searching for the features that provide good performance for SVM, KNN, and Random forest classifiers.…”

Section: Hybrid Machine Learning and Metaheuristics Approaches To Spa...mentioning

confidence: 99%

Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering

et al. 2022

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Spam represents a genuine irritation for email users, since it often disturbs them during their work or free time. Machine learning approaches are commonly utilized as the engine of spam detection solutions, as they are efficient and usually exhibit a high degree of classification accuracy. Nevertheless, it sometimes happens that good messages are labeled as spam and, more often, some spam emails enter into the inbox as good ones. This manuscript proposes a novel email spam detection approach by combining machine learning models with an enhanced sine cosine swarm intelligence algorithm to counter the deficiencies of the existing techniques. The introduced novel sine cosine was adopted for training logistic regression and for tuning XGBoost models as part of the hybrid machine learning-metaheuristics framework. The developed framework has been validated on two public high-dimensional spam benchmark datasets (CSDMC2010 and TurkishEmail), and the extensive experiments conducted have shown that the model successfully deals with high-degree data. The comparative analysis with other cutting-edge spam detection models, also based on metaheuristics, has shown that the proposed hybrid method obtains superior performance in terms of accuracy, precision, recall, f1 score, and other relevant classification metrics. Additionally, the empirically established superiority of the proposed method is validated using rigid statistical tests.

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A Privacy-Preserving Machine Learning Ensemble for Spam Detection

Upadhyaya

2023

2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA)

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Spam Detection using ANN and ABC Algorithm

Cited by 5 publications

References 24 publications

A systematic literature review on spam content detection and classification

A systematic literature review on spam content detection and classification

Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering

A Privacy-Preserving Machine Learning Ensemble for Spam Detection

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