Improving Knowledge Based Spam Detection Methods: The Effect of Malicious Related Features in Imbalance Data Distribution

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International Journal of Communications, Network and System Sciences

ISSN Print: 1913-3715
ISSN Online: 1913-3723
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"Improving Knowledge Based Spam Detection Methods: The Effect of Malicious Related Features in Imbalance Data Distribution"
written by Jafar Alqatawna, Hossam Faris, Khalid Jaradat, Malek Al-Zewairi, Omar Adwan,
published by International Journal of Communications, Network and System Sciences, Vol.8 No.5, 2015

has been cited by the following article(s):

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[1]	Shapley value-based approaches to explain the robustness of classifiers in machine learning
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[2]	Enhancing representation in the context of multiple-channel spam filtering
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[3]	A novel synthetic minority oversampling technique based on relative and absolute densities for imbalanced classification
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[4]	Email Filtering Using Hybrid Feature Selection Model
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[5]	Hybrid big bang–big crunch with ant colony optimization for email spam detection
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[6]	Detecting Fraud Job Recruitment Using Features Reflecting from Real-world Knowledge of Fraud
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[7]	Spam Detection Based on Feature Evolution to Deal with Concept Drift
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[8]	Review of Classification Methods on Unbalanced Data Sets
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[9]	Spam Detection Based on Feature Evolution to Deal with Concept Drift.
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[10]	The Emotions of Science: Using Social Media to Gauge Public Emotions Toward Research Topics
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[11]	A feature-centric spam email detection model using diverse supervised machine learning algorithms
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[12]	Towards Automated Comprehensive Feature Engineering for Spam Detection.
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[13]	Multi Sampling Random Subspace Ensemble for Imbalanced Data Stream Classification
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[14]	Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context
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[15]	Towards Automated Comprehensive Feature Engineering for Spam Detection
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[16]	Classification-based approach for Question Answering Systems: Design and Application in HR operations
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[17]	Análisis de la influencia del desbalanceo en conjuntos de datos para la clasificación de imágenes mediante redes neuronales convolucionales.
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[18]	An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks
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[19]	Cost-Sensitive Learner on Hybrid SMOTE-Ensemble Approach to Predict Software Defects
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[20]	Improving email spam detection using content based feature engineering approach
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[21]	USING MODIFIED BAT ALGORITHM TO TRAIN NEURAL NETWORKS FOR SPAM DETECTION.
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[22]	EMFET: E-mail Features Extraction Tool.
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[23]	A new semantic attribute deep learning with a linguistic attribute hierarchy for spam detection
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[24]	Online Social Networks Security: Threats, Attacks, and Future Directions
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[25]	Spam profile detection in social networks based on public features
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[26]	EMFET: E-mail Features Extraction Tool
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[27]	Spotting the Islamist Radical within: Religious Extremists Profiling in the United State
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[28]	Spam Filtering based on Naïve Bayesian with Information Gain and Ant Colony System
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[29]	Optimizing Feedforward Neural Networks Using Biogeography Based Optimization for E-Mail Spam Identification
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[30]	EMAIL SPAM CLASSIFICATION USING HYBRID APPROACH OF RBF NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION
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[31]	Voting-based Classification for E-mail Spam Detection
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[32]	Statistical Detection of Online Drifting Twitter Spam: Invited Paper
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[33]	A Hybrid Approach Based on Particle Swarm Optimization and Random Forests for E-Mail Spam Filtering
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[34]	Categorizing Blog Spam
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[35]	A Preliminary Analysis of Drive-by Email Attacks in Educational Institutes
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[36]	Analyzing CyberCrimes Strategies: The Case of Phishing Attack
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[37]	Incremental information gain analysis of input attribute impact on RBF-kernel SVM spam detection
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[38]	Auto-tuning of parameters in hybrid sampling method for class imbalance problem
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[39]	Voting-based Classification for E-mail Spam Detection.
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[40]	Optimizing Feedforward neural networks using Krill Herd algorithm for E-mail spam detection
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[41]	Parallel Ensemble Feature Subset Selection based Deep Learning Approach for Imbalanced High Dimensional Cancer Datasets


[42]	Performance Evaluation of Machine Learning Algorithms on Textual Datasets for Spam Email Classification

[1]	A Multilingual Spam Reviews Detection Based on Pre-Trained Word Embedding and Weighted Swarm Support Vector Machines IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3293641

[2]	A new Monte Carlo sampling method based on Gaussian Mixture Model for imbalanced data classification Mathematical Biosciences and Engineering, 2023 DOI:10.3934/mbe.2023794

[3]	Email Filtering Using Hybrid Feature Selection Model Computer Modeling in Engineering & Sciences, 2022 DOI:10.32604/cmes.2022.020088

[4]	Enhancing representation in the context of multiple-channel spam filtering Information Processing & Management, 2022 DOI:10.1016/j.ipm.2021.102812

[5]	A Comprehensive Survey of Phishing Email Detection and Protection Techniques Information Security Journal: A Global Perspective, 2022 DOI:10.1080/19393555.2021.1959678

[6]	Spam Email Classification by Hybrid Feature Selection with Advanced Machine learning Algorithm – Future Perspective Journal of Soft Computing Paradigm, 2022 DOI:10.36548/jscp.2022.2.002

[7]	Review of Classification Methods on Unbalanced Data Sets IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3074243

[8]	A feature-centric spam email detection model using diverse supervised machine learning algorithms The Electronic Library , 2020 DOI:10.1108/EL-07-2019-0181

[9]	An Intelligent System for Spam Detection and Identification of the most Relevant Features based on Evolutionary Random Weight Networks Information Fusion, 2018 DOI:10.1016/j.inffus.2018.08.002

[10]	Social Media Shaping e-Publishing and Academia 2017 DOI:10.1007/978-3-319-55354-2_10

[11]	Spam profile detection in social networks based on public features 2017 8th International Conference on Information and Communication Systems (ICICS), 2017 DOI:10.1109/IACS.2017.7921959

[12]	A new semantic attribute deep learning with a linguistic attribute hierarchy for spam detection 2017 International Joint Conference on Neural Networks (IJCNN), 2017 DOI:10.1109/IJCNN.2017.7966343

[13]	Improving email spam detection using content based feature engineering approach 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2017 DOI:10.1109/AEECT.2017.8257764

[14]	Spotting the Islamist Radical within: Religious Extremists Profiling in the United State Procedia Computer Science, 2017 DOI:10.1016/j.procs.2017.08.336

[15]	Statistical Detection of Online Drifting Twitter Spam Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security - ASIA CCS '16, 2016 DOI:10.1145/2897845.2897928

[16]	Incremental information gain analysis of input attribute impact on RBF-kernel SVM spam detection 2016 IEEE Congress on Evolutionary Computation (CEC), 2016 DOI:10.1109/CEC.2016.7743901

[17]	A Preliminary Analysis of Drive-by Email Attacks in Educational Institutes 2016 Cybersecurity and Cyberforensics Conference (CCC), 2016 DOI:10.1109/CCC.2016.16

[18]	Analyzing CyberCrimes Strategies: The Case of Phishing Attack 2016 Cybersecurity and Cyberforensics Conference (CCC), 2016 DOI:10.1109/CCC.2016.25

[19]	Auto-tuning of parameters in hybrid sampling method for class imbalance problem 2016 International Computer Science and Engineering Conference (ICSEC), 2016 DOI:10.1109/ICSEC.2016.7859941

[20]	Optimizing Feedforward Neural Networks Using Biogeography Based Optimization for E-Mail Spam Identification International Journal of Communications, Network and System Sciences, 2016 DOI:10.4236/ijcns.2016.91002

[21]	Statistical Detection of Online Drifting Twitter Spam Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, 2016 DOI:10.1145/2897845.2897928

[22]	Optimizing Feedforward neural networks using Krill Herd algorithm for E-mail spam detection 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2015 DOI:10.1109/AEECT.2015.7360576

Follow SCIRP

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	+86 18163351462(WhatsApp)
	1655362766

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