Spam Email Detection Using Deep Support Vector Machine, Support Vector Machine and Artificial Neural Network

被引:5
|
作者
Roy, Sanjiban Sekhar [1 ]
Sinha, Abhishek [1 ]
Roy, Reetika [1 ]
Barna, Cornel [2 ]
Samui, Pijush [3 ]
机构
[1] VIT Univ, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India
[2] Aurel Vlaicu Univ Arad, Automat & Appl Informat, Arad, Romania
[3] NIT Patna, Dept Civil Engn, Patna, Bihar, India
来源
SOFT COMPUTING APPLICATIONS, SOFA 2016, VOL 2 | 2018年 / 634卷
关键词
Spam; Classification; Deep Support Vector Machine; Support Vector Machine; Artificial Neural Network;
D O I
10.1007/978-3-319-62524-9_13
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Emails are a very important part of our life today for information sharing. It is used for both personal communication as well as business purposes. But the internet also opens up the prospect of an enormous amount of junk and useless information which overwhelms and irritates us. These unnecessary and unsolicited emails are what comprise of spam. This study presents the application of a classification model to classify spam emails from using a model-Deep Support Vector Machine (Deep SVM). Moreover, other classifier models like Support Vector Machine (SVM), Artificial Neural Network models have also been implemented to compare the performance of proposed Deep SVM model. Furthermore analysis has been done to compare all the performances using available numerical statistics obtained from these models to find the best model for the purpose. Spam filtering is a very essential feature in most email services and thus effective spam classification models are pertinent to the current digital communication scenario and various work has been done in this area.
引用
收藏
页码:162 / 174
页数:13
相关论文
共 50 条
  • [1] Automated plant identification using artificial neural network and support vector machine
    Jye, Kho Soon
    Manickam, Sugumaran
    Malek, Sorayya
    Mosleh, Mogeeb
    Dhillon, Sarinder Kaur
    FRONTIERS IN LIFE SCIENCE, 2017, 10 (01): : 98 - 107
  • [2] DETECTION OF MAMMOGRAPHIC CANCER USING SUPPORT VECTOR MACHINE AND DEEP NEURAL NETWORK
    Krishna, Timmana Hari
    Rajabhushnam, C.
    JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, 2019, 14 (06): : 156 - 167
  • [3] Detecting SIM Box Fraud by Using Support Vector Machine and Artificial Neural Network
    Sallehuddin, Roselina
    Ibrahim, Subariah
    Zain, Azlan Mohd
    Elmi, Abdikarim Hussein
    JURNAL TEKNOLOGI, 2015, 74 (01):
  • [4] APPROXIMATING SWAT MODEL USING ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE
    Zhang, Xuesong
    Srinivasan, Raghavan
    Van Liew, Michael
    JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 2009, 45 (02): : 460 - 474
  • [5] Crop Prediction Using Artificial Neural Network and Support Vector Machine
    Fegade, Tanuja K.
    Pawar, B. V.
    DATA MANAGEMENT, ANALYTICS AND INNOVATION, ICDMAI 2019, VOL 2, 2020, 1016 : 311 - 324
  • [6] Cancer Detection Using Aritifical Neural Network and Support Vector Machine: A Comparative Study
    Ubaidillah, Sharifah Hafizah Sy Ahmad
    Sallehuddin, Roselina
    Ali, Nor Azizah
    JURNAL TEKNOLOGI, 2013, 65 (01):
  • [7] Support Vector Machine Based Spam SMS Detection
    Tekerek, Adem
    JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 2019, 22 (03): : 779 - 784
  • [8] Approximating support vector machine with artificial neural network for fast prediction
    Kang, Seokho
    Cho, Sungzoon
    EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (10) : 4989 - 4995
  • [9] ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE IN FLOOD FORECASTING: A REVIEW
    Suliman, Azizah
    Nazri, Nursyazana
    Othman, Marini
    Malek, Marlinda Abdul
    Ku-Mahamud, Ku Ruhana
    COMPUTING & INFORMATICS, 4TH INTERNATIONAL CONFERENCE, 2013, 2013, : 327 - +
  • [10] Site classification with support vector machine and artificial neural network
    Cosenza, Diogo Nepomuceno
    Leite, Helio Garcia
    Marcatti, Gustavo Eduardo
    Breda Binoti, Daniel Henrique
    Mazon de Alcantara, Aline Edwiges
    Rode, Rafael
    SCIENTIA FORESTALIS, 2015, 43 (108): : 955 - 963