A Comprehensive Survey for Intelligent Spam Email Detection
暂无分享,去创建一个
Asif Karim | Mamoun Alazab | Bharanidharan Shanmugam | Sami Azam | Krishnan Kannoorpatti | M. Alazab | B. Shanmugam | S. Azam | Asif Karim | K. Kannoorpatti | Bharanidharan Shanmugam
[1] Dong Yu,et al. Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..
[2] Steven Wiltshire,et al. Expectation Maximization Algorithm (E‐M Algorithm) , 2014 .
[3] Md. Rafiqul Islam,et al. A multi-tier phishing detection and filtering approach , 2013, J. Netw. Comput. Appl..
[4] Alan S. Perelson,et al. Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.
[5] Haiyan Wang,et al. An Anti-spam Filtering System Based on the Naive Bayesian Classifier and Distributed Checksum Clearinghouse , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.
[6] Gurjot Kaur,et al. E-Mail Spam Detection Using SVM and RBF , 2016 .
[7] Manjunath R. Kounte,et al. Comparative study of self-organizing map and deep self-organizing map using MATLAB , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).
[8] Gianluca Stringhini,et al. B@bel: Leveraging Email Delivery for Spam Mitigation , 2012, USENIX Security Symposium.
[9] B D Satoto,et al. Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster , 2018, IOP Conference Series: Materials Science and Engineering.
[10] Lap Trung Nguyen,et al. Using WordNet Similarity and Translations to Create Synsets for Ontology-Based Vietnamese WordNet , 2016, 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI).
[11] Honggang Zhang,et al. Image spam classification based on convolutional neural network , 2016, 2016 International Conference on Machine Learning and Cybernetics (ICMLC).
[12] Liang Ting,et al. Spam Feature Selection Based on the Improved Mutual Information Algorithm , 2012, 2012 Fourth International Conference on Multimedia Information Networking and Security.
[13] Richard D. Kortum. Hyperonyms and Hyponyms , 2013 .
[14] Rong Jin,et al. Understanding bag-of-words model: a statistical framework , 2010, Int. J. Mach. Learn. Cybern..
[15] Qing Yang,et al. A support vector machine based naive Bayes algorithm for spam filtering , 2016, 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC).
[16] Allan Liska,et al. Chapter 1 – Understanding DNS , 2016 .
[17] Stephen Winters-Hilt,et al. Clustering via support vector machine boosting with simulated annealing , 2017 .
[18] Jonathan Oliver,et al. Mining Malware to Detect Variants , 2014, 2014 Fifth Cybercrime and Trustworthy Computing Conference.
[19] Carmen Paz Suárez Araujo,et al. Self-Organizing Maps in the Design of Anti-spam Filters - A Proposal based on Thematic Categories , 2016, IJCCI.
[20] Emilio Corchado,et al. Clustering Ensemble for Spam Filtering , 2011, HAIS.
[21] Sahin Isik,et al. The investigation on the effect of feature vector dimension for spam email detection with a new framework , 2014, 2014 9th Iberian Conference on Information Systems and Technologies (CISTI).
[22] K. P. Soman,et al. Deep Learning Approach for Intelligent Intrusion Detection System , 2019, IEEE Access.
[23] Aziz Qaroush,et al. Identifying spam e-mail based-on statistical header features and sender behavior , 2012, CUBE.
[24] Doaa Hassan,et al. Investigating the Effect of Combining Text Clustering with Classification on Improving Spam Email Detection , 2016, ISDA.
[25] Ibrahim F. Moawad,et al. Semantic-Based Feature Reduction Approach for E-mail Classification , 2016, AISI.
[26] Florentino Fernández Riverola,et al. Using evolutionary computation for discovering spam patterns from e-mail samples , 2018, Inf. Process. Manag..
[27] Fadi Thabtah,et al. An Experimental Study for Assessing Email Classification Attributes Using Feature Selection Methods , 2014, 2014 3rd International Conference on Advanced Computer Science Applications and Technologies.
[28] Vinesh Jain,et al. POS Tagging Approaches: A Comparison , 2015 .
[29] Lucian Ilie. Regular Expression Matching , 2008, Encyclopedia of Algorithms.
[30] Danny Hendler,et al. Early detection of spamming accounts in large-Scale service provider networks , 2017, Knowl. Based Syst..
[31] Kapil Sharma,et al. Bayesian spam classification: Time efficient radix encoded fragmented database approach , 2014, 2014 International Conference on Computing for Sustainable Global Development (INDIACom).
[32] Mike Barley,et al. Evaluating the Seeding Genetic Algorithm , 2013, Australasian Conference on Artificial Intelligence.
[33] Issam Dagher,et al. Ham-Spam Filtering Using Different PCA Scenarios , 2016, 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES).
[34] Nizar Bouguila,et al. Content-based spam filtering using hybrid generative discriminative learning of both textual and visual features , 2012, 2012 IEEE International Symposium on Circuits and Systems.
[35] Asif Karim,et al. Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots , 2018, RTIP2R.
[36] Adi Wijaya,et al. Hybrid decision tree and logistic regression classifier for email spam detection , 2016, 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE).
[37] Richa Tiwari,et al. Information extraction from spam emails using stylistic and semantic features to identify spammers , 2011, 2011 IEEE International Conference on Information Reuse & Integration.
[38] Ali Selamat,et al. Hybrid email spam detection model with negative selection algorithm and differential evolution , 2014, Eng. Appl. Artif. Intell..
[39] Tatsuya Mori,et al. On the effectiveness of IP reputation for spam filtering , 2010, 2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010).
[40] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[41] Zhi-Hua Zhou,et al. Disagreement-based Semi-supervised Learning , 2013 .
[42] Vishal Gupta,et al. Text Stemming , 2016, ACM Comput. Surv..
[43] Tariq Rashid. Make your own neural network : a gentle journey through the mathematics of neural networks, and making your own using the Python computer language , 2016 .
[44] Mitsuaki Akiyama,et al. DomainChroma: Building actionable threat intelligence from malicious domain names , 2018, Comput. Secur..
[45] Stephen Winters-Hilt,et al. SVM clustering , 2007, BMC Bioinformatics.
[46] Pijush Samui,et al. Spam Email Detection Using Deep Support Vector Machine, Support Vector Machine and Artificial Neural Network , 2016, SOFA.
[47] Mohamed Medhat Gaber,et al. Random forests: from early developments to recent advancements , 2014 .
[48] George Forman,et al. An Extensive Empirical Study of Feature Selection Metrics for Text Classification , 2003, J. Mach. Learn. Res..
[49] R. Sathya,et al. Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification , 2013 .
[50] O. A. Okunade. Manipulating E-Mail Server Feedback for Spam Prevention , 2017 .
[51] Ajay Sharma,et al. A Novel Method for Detecting Spam Email using KNN Classification with Spearman Correlation as Distance Measure , 2016 .
[52] Wen-Guey Tzeng,et al. An online subject-based spam filter using natural language features , 2017, 2017 IEEE Conference on Dependable and Secure Computing.
[53] Ruihu Wang,et al. AdaBoost for Feature Selection, Classification and Its Relation with SVM, A Review , 2012 .
[54] Ashraf Darwish,et al. A Survey of Machine Learning Techniques for Spam Filtering , 2012 .
[55] Asif Karim. Multi-layer Masking of Character Data with a Visual Image Key , 2017 .
[56] James Large,et al. The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts , 2017, ArXiv.
[57] Abdelmunem Abuhasan,et al. An intelligent classification model for phishing email detection , 2016, ArXiv.
[58] Santosh S. Vempala,et al. Filtering spam with behavioral blacklisting , 2007, CCS '07.
[59] Ying Tan,et al. Extracting discriminative information from e-mail for spam detection inspired by Immune System , 2010, IEEE Congress on Evolutionary Computation.
[60] Chih-Fong Tsai,et al. The distance function effect on k-nearest neighbor classification for medical datasets , 2016, SpringerPlus.
[61] M. Basavaraju,et al. A Novel Method of Spam Mail Detection using Text Based Clustering Approach , 2010 .
[62] Andrew H. Sung,et al. Detection of Phishing Attacks: A Machine Learning Approach , 2008, Soft Computing Applications in Industry.
[63] Xiaowei Yang,et al. SocialFilter: Introducing social trust to collaborative spam mitigation , 2010, 2011 Proceedings IEEE INFOCOM.
[64] Asif Karim. A Cryptographic Application for Secure Information Transfer in a Linux Network Environment , 2016 .
[65] Das Amrita,et al. Mining Association Rules between Sets of Items in Large Databases , 2013 .
[66] Reza Moradi Rad,et al. A survey of image spamming and filtering techniques , 2011, Artificial Intelligence Review.
[67] Mazleena Salleh,et al. The Effect of Feature Selection on Phish Website Detection , 2015 .
[68] Shailendra S. Aote,et al. A Brief Review on Particle Swarm Optimization : Limitations & Future Directions , 2013 .
[69] Shikhar Seth,et al. Multimodal Spam Classification Using Deep Learning Techniques , 2017, 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).
[70] Marie-Francine Moens,et al. Highly discriminative statistical features for email classification , 2012, Knowledge and Information Systems.
[71] Narasimham Challa,et al. A practical approach to E-mail spam filters to protect data from advanced persistent threat , 2016, 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT).
[72] Philip Sedgwick,et al. Pearson’s correlation coefficient , 2012, BMJ : British Medical Journal.
[73] C. Chellappan,et al. Detecting Malicious URLs in E-mail – An Implementation , 2013 .
[74] Mark Allman,et al. A large-scale empirical analysis of email spam detection through network characteristics in a stand-alone enterprise , 2014, Comput. Networks.
[75] Zili Zhang,et al. An Evidential Spam-Filtering Framework , 2016, Cybern. Syst..
[76] Hiral D. Padhiyar,et al. An Improved Expectation Maximization based Semi- Supervised Email Classification using Naïve Bayes and K- Nearest Neighbor , 2014 .
[77] Yong Hu,et al. A scalable intelligent non-content-based spam-filtering framework , 2010, Expert Syst. Appl..
[78] Ibrahim F. Moawad,et al. Efficient email classification approach based on semantic methods , 2018, Ain Shams Engineering Journal.
[79] Xin Jin,et al. Partitional Clustering , 2021, Encyclopedia of Machine Learning.
[80] Jurandy Almeida,et al. Spam filtering: how the dimensionality reduction affects the accuracy of Naive Bayes classifiers , 2011, Journal of Internet Services and Applications.
[81] Feng Qian,et al. A case for unsupervised-learning-based spam filtering , 2010, SIGMETRICS '10.
[82] Maozhen Li,et al. A survey of emerging approaches to spam filtering , 2012, CSUR.
[83] Sujeet More,et al. Data mining with machine learning applied for email deception , 2013, 2013 International Conference on Optical Imaging Sensor and Security (ICOSS).
[84] Aderemi Oluyinka Adewumi,et al. A hybrid firefly and support vector machine classifier for phishing email detection , 2016, Kybernetes.
[85] Jussara M. Almeida,et al. Adaptive spammer detection at the source network , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).
[86] Izzat Alsmadi,et al. Clustering and classification of email contents , 2015, J. King Saud Univ. Comput. Inf. Sci..
[87] Václav Snásel,et al. SPAM DETECTION USING DATA COMPRESSION AND SIGNATURES , 2013, Cybern. Syst..
[88] T. L. McCluskey,et al. An assessment of features related to phishing websites using an automated technique , 2012, 2012 International Conference for Internet Technology and Secured Transactions.
[89] Aliaksandr Barushka,et al. Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks , 2018, Applied Intelligence.
[90] Josef Pieprzyk,et al. Critical analysis of spam prevention techniques , 2011, 2011 Third International Workshop on Security and Communication Networks (IWSCN).
[91] Saïd Salhi,et al. A hybrid algorithm for identifying global and local minima when optimizing functions with many minima , 2004, Eur. J. Oper. Res..
[92] Nauman Aslam,et al. Detection of online phishing email using dynamic evolving neural network based on reinforcement learning , 2018, Decis. Support Syst..
[93] Mamoun Alazab,et al. Profiling and classifying the behavior of malicious codes , 2015, J. Syst. Softw..
[94] Po-Ching Lin,et al. Detecting spamming activities by network monitoring with Bloom filters , 2013, 2013 15th International Conference on Advanced Communications Technology (ICACT).
[95] Kunjali Pawar,et al. Pattern classification under attack on spam filtering , 2015, 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN).
[96] Teng-Sheng Moh,et al. Content Based Spam E-mail Filtering , 2016, 2016 International Conference on Collaboration Technologies and Systems (CTS).
[97] Johannes Fürnkranz,et al. Large-Scale Multi-label Text Classification - Revisiting Neural Networks , 2013, ECML/PKDD.
[98] Robbie T. Nakatsu. Information Visualizations Used to Avoid the Problem of Overfitting in Supervised Machine Learning , 2017, HCI.
[99] Gang Wang,et al. Revisiting Email Spoofing Attacks , 2018, ArXiv.
[100] Bartosz Krawczyk,et al. APPLICATION OF ADAPTIVE SPLITTING AND SELECTION CLASSIFIER TO THE SPAM FILTERING PROBLEM , 2013, Cybern. Syst..
[101] Xiaoming Fu,et al. Identity based email sender authentication for spam mitigation , 2013, Eighth International Conference on Digital Information Management (ICDIM 2013).
[102] Yong Deng,et al. A New Method to Determine Generalized Basic Probability Assignment in the Open World , 2019, IEEE Access.
[103] John M. Hancock. Self‐Organizing Map (Kohonen Map, SOM) , 2004 .
[104] Pedro Sousa,et al. A Collaborative Approach for Spam Detection , 2010, 2010 2nd International Conference on Evolving Internet.
[105] M. Tariq Banday,et al. Analyzing Internet e-mail date-spoofing , 2011, Digit. Investig..
[106] Yang Xiang,et al. Email classification using data reduction method , 2010, 2010 5th International ICST Conference on Communications and Networking in China.
[107] Amit P. Sheth,et al. Machine learning for Internet of Things data analysis: A survey , 2017, Digit. Commun. Networks.
[108] Jiyong Jang,et al. Experimental study of fuzzy hashing in malware clustering analysis , 2015 .
[109] A. Darwish. Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications , 2018, Future Computing and Informatics Journal.
[110] Jemal H. Abawajy,et al. Phishing Email Feature Selection Approach , 2011, 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications.
[111] Cristinel CONSTANTIN. Using the Logistic Regression model in supporting decisions of establishing marketing strategies , 2015 .
[112] Gundeep Singh Bindra,et al. Inbound & Outbound Email Traffic Analysis and Its SPAM Impact , 2012, 2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks.
[113] Robert E. Mercer,et al. Classifying Spam Emails Using Text and Readability Features , 2013, 2013 IEEE 13th International Conference on Data Mining.
[114] Ali Shafigh Aski,et al. Proposed efficient algorithm to filter spam using machine learning techniques , 2016 .
[115] Raihana Ferdous,et al. Analysis and Protection of SIP based Services , 2014 .
[116] Oluwafemi Osho,et al. Comparative Analysis of Classification Algorithms for Email Spam Detection , 2018 .
[117] Jonathan Currie,et al. Dynamic Event Detection Using a Distributed Feature Selection Based Machine Learning Approach in a Self-Healing Microgrid , 2018, IEEE Transactions on Power Systems.
[118] A Nursikuwagus,et al. Prediction Student Eligibility in Vocation School with Naïve-Byes Decision Algorithm , 2018 .
[119] Gonzalo Álvarez,et al. Word sense disambiguation for spam filtering , 2012, Electron. Commer. Res. Appl..
[120] G. Aghila,et al. Spam filtering: Comparative analysis of filtering techniques , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).
[121] Sandro Sperandei,et al. Understanding logistic regression analysis , 2014, Biochemia medica.
[122] Sudipta Roy,et al. An optimized k-NN classifier based on minimum spanning tree for email filtering , 2014, 2014 2nd International Conference on Business and Information Management (ICBIM).
[123] Suku Nair,et al. A comparison of machine learning techniques for phishing detection , 2007, eCrime '07.
[124] P BabuAnto,et al. Ambiguities in Natural Language Processing , 2014 .
[125] Himansu Sekhar Behera,et al. A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges , 2015 .
[126] Ngoc Thanh Nguyen,et al. A combined negative selection algorithm-particle swarm optimization for an email spam detection system , 2015, Eng. Appl. Artif. Intell..
[127] Simon Hegelich,et al. Decision Trees and Random Forests: Machine Learning Techniques to Classify Rare Events , 2016 .
[128] Zhiyuan Tan,et al. Towards Designing an Email Classification System Using Multi-view Based Semi-supervised Learning , 2014, 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications.
[129] Youssef Iraqi,et al. Phishing Detection: A Literature Survey , 2013, IEEE Communications Surveys & Tutorials.
[130] Jason Brownlee,et al. Clever Algorithms: Nature-Inspired Programming Recipes , 2012 .
[131] Dr. S. M. Chaware,et al. An Efficient Framework for Spam Mail Detection in Attachments using NLP , 2016 .
[132] Calton Pu,et al. A study on evolution of email spam over fifteen years , 2013, CollaborateCom 2013.
[133] Ganthan Narayana Samy,et al. Heuristic systematic model based guidelines for phishing victims , 2016, 2016 IEEE Annual India Conference (INDICON).
[134] Fabio Roli,et al. A survey and experimental evaluation of image spam filtering techniques , 2011, Pattern Recognit. Lett..
[135] Bharanidharan Shanmugam,et al. An Efficient Method for Detecting Fraudulent Transactions Using Classification Algorithms on an Anonymized Credit Card Data Set , 2017, ISDA.
[136] Ian T. Jolliffe,et al. Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.
[137] Yiyu Yao,et al. Cost-sensitive three-way email spam filtering , 2013, Journal of Intelligent Information Systems.
[138] Mariette Awad,et al. Ham or spam? A comparative study for some content-based classification algorithms for email filtering , 2014, MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference.
[139] Concha Bielza,et al. Discrete Bayesian Network Classifiers , 2014, ACM Comput. Surv..
[140] Eman M. Bahgat,et al. An E-mail Filtering Approach Using Classification Techniques , 2015, AISI.
[141] Jose Miguel Puerta,et al. Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets , 2011, Expert Syst. Appl..
[142] Bilal Bahaa Zaidan,et al. Impact of spam advertisement through e-mail: A study to assess the influence of the anti-spam on the e-mail marketing , 2010 .
[143] Milagros Rivera Sánchez,et al. An Anti-spam Framework for Singapore , 2003 .
[144] Ernesto Damiani,et al. An Open Digest-based Technique for Spam Detection , 2004, PDCS.
[145] R. Kishore Kumar,et al. Comparative Study on Email Spam Classifier using Data Mining Techniques , 2012 .
[146] Islam A. T. F. Taj-Eddin,et al. Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks , 2013 .
[147] Feng Yu,et al. A Content-Based Phishing Email Detection Method , 2017, 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C).
[148] Azadeh Shakery,et al. Content-based concept drift detection for Email spam filtering , 2010, 2010 5th International Symposium on Telecommunications.
[149] 梁仲文.,et al. An analysis of the impact of phishing and anti-phishing related announcements on market value of global firms , 2009 .
[150] Adriano Veloso,et al. Rule-Based Active Sampling for Learning to Rank , 2011, ECML/PKDD.
[151] Abiodun Modupe,et al. Feature selection and support vector machine hyper-parameter optimisation for spam detection , 2016, 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech).
[152] Doaa Hassan,et al. On Determining the Most Effective Subset of Features for Detecting Phishing Websites , 2015 .
[153] Chih-Yi Chiu,et al. Learning to Index for Nearest Neighbor Search , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[154] Danny Bradbury. Can we make email secure? , 2014, Netw. Secur..
[155] Igor Santos,et al. Study on the effectiveness of anomaly detection for spam filtering , 2014, Inf. Sci..
[156] Nasrullah Memon,et al. Detection of Fraudulent Emails by Employing Advanced Feature Abundance , 2014 .
[157] Martin F. Porter,et al. An algorithm for suffix stripping , 1997, Program.
[158] José Ramon Méndez,et al. A new semantic-based feature selection method for spam filtering , 2019, Appl. Soft Comput..
[159] Gökhan Dalkiliç,et al. Detecting spam through their Sender Policy Framework records , 2015, Secur. Commun. Networks.
[160] Aiko Pras,et al. Filtering spam from bad neighborhoods , 2010, Int. J. Netw. Manag..
[161] Roderic Broadhurst,et al. Malicious Spam Emails Developments and Authorship Attribution , 2013, 2013 Fourth Cybercrime and Trustworthy Computing Workshop.
[162] Bharanidharan Shanmugam,et al. An Intelligent Spam Detection Model Based on Artificial Immune System , 2019, Inf..
[163] Mee Hong Ling,et al. A Survey on Reinforcement Learning Models and Algorithms for Traffic Signal Control , 2017, ACM Comput. Surv..
[164] Haider M. Al-Mashhadi,et al. A Survey of Email Service; Attacks, Security Methods and Protocols , 2017 .
[165] Harry Wechsler,et al. Spam detection using Random Boost , 2012, Pattern Recognit. Lett..
[166] Aakanksha Sharaff,et al. Comparative Study of Classification Algorithms for Spam Email Detection , 2016 .
[167] Mona Mojdeh. Personal Email Spam Filtering with Minimal User Interaction , 2012 .
[168] Dr. M. Nazreen Banu,et al. A Comprehensive Study of Phishing Attacks , 2013 .
[169] Reshma Varghese,et al. Efficient Feature Set for Spam Email Filtering , 2017, 2017 IEEE 7th International Advance Computing Conference (IACC).
[170] Konstantinos E. Psannis,et al. Defending against phishing attacks: taxonomy of methods, current issues and future directions , 2017, Telecommunication Systems.
[171] R. H. Schmitt,et al. Uncertainty-based test planning using dempster-shafer theory of evidence , 2017, 2017 2nd International Conference on System Reliability and Safety (ICSRS).
[172] Michael Sirivianos,et al. SocialFilter: Introducing social trust to collaborative spam mitigation , 2010, 2011 Proceedings IEEE INFOCOM.
[173] Zhansheng Duan,et al. Generalized Principal Component Analysis , 2017 .
[174] Robert E. Mercer,et al. Personalized Spam Filtering with Natural Language Attributes , 2013, 2013 12th International Conference on Machine Learning and Applications.
[175] Koen Vanhoof,et al. Detecting malicious URLs using machine learning techniques , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).
[176] Cheng-Chi Lee,et al. An efficient incremental learning mechanism for tracking concept drift in spam filtering , 2017, PloS one.
[177] Tiago A. Almeida,et al. Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering , 2016, Knowl. Based Syst..
[178] Wenjia Wang. Heterogeneous Bayesian ensembles for classifying spam emails , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).
[179] Radhika Ranjan Roy. Basic Session Initiation Protocol , 2016 .
[180] Ja'far Alqatawna,et al. Analyzing CyberCrimes Strategies: The Case of Phishing Attack , 2016, 2016 Cybersecurity and Cyberforensics Conference (CCC).
[181] Roderic Broadhurst,et al. Towards a Feature Rich Model for Predicting Spam Emails containing Malicious Attachments and URLs , 2014 .
[182] Antony Selvadoss Thanamani,et al. K-Means Document Clustering using Vector Space Model , 2015 .
[183] Kensuke Fukuda,et al. Clustering Spam Campaigns with Fuzzy Hashing , 2014, AINTEC.
[184] Xiaojin Zhu,et al. Semi-Supervised Learning , 2010, Encyclopedia of Machine Learning.