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
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
  • [41] Adversarial Spam Detection Using the Randomized Hough Transform-Support Vector Machine
    DeBarr, Dave
    Sun, Hao
    Wechsler, Harry
    2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1, 2013, : 299 - 304
  • [42] Spam detection on Twitter using a support vector machine and users’ features by identifying their interactions
    Saleh Beyt Sheikh Ahmad
    Mahnaz Rafie
    Seyed Mojtaba Ghorabie
    Multimedia Tools and Applications, 2021, 80 : 11583 - 11605
  • [43] Detection method of unlearned pattern using support vector machine in damage classification based on deep neural network
    Kohiyama, Masayuki
    Oka, Kazuya
    Yamashita, Takuzo
    STRUCTURAL CONTROL & HEALTH MONITORING, 2020, 27 (08):
  • [44] Defect detection method using deep convolutional neural network, support vector machine and template matching techniques
    Fusaomi Nagata
    Kenta Tokuno
    Kazuki Mitarai
    Akimasa Otsuka
    Takeshi Ikeda
    Hiroaki Ochi
    Keigo Watanabe
    Maki K. Habib
    Artificial Life and Robotics, 2019, 24 : 512 - 519
  • [45] Defect detection method using deep convolutional neural network, support vector machine and template matching techniques
    Nagata, Fusaomi
    Tokuno, Kenta
    Mitarai, Kazuki
    Otsuka, Akimasa
    Ikeda, Takeshi
    Ochi, Hiroaki
    Watanabe, Keigo
    Habib, Maki K.
    ARTIFICIAL LIFE AND ROBOTICS, 2019, 24 (04) : 512 - 519
  • [46] Osteoporosis Risk Prediction Using Enhanced Support Vector Machine over Artificial Neural Network
    Jagadeesh, A.
    Kumar, Senthil S.
    JOURNAL OF PHARMACEUTICAL NEGATIVE RESULTS, 2022, 13 : 1602 - 1611
  • [47] Classification of Tumors and It Stages in Brain MRI Using Support Vector Machine and Artificial Neural Network
    Ahmmed, Rasel
    Sen Swakshar, Anirban
    Hossain, Md. Foisal
    Rafiq, Md. Abdur
    2017 INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND COMMUNICATION ENGINEERING (ECCE), 2017, : 229 - 234
  • [48] Fuzzy neural network classification design using support vector machine
    Lin, CT
    Yeh, CM
    Hsu, CF
    2004 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL 5, PROCEEDINGS, 2004, : 724 - 727
  • [49] Decomposition and Symmetric Kernel Deep Neural Network Fuzzy Support Vector Machine
    El Moutaouakil, Karim
    Roudani, Mohammed
    Ouhmid, Azedine
    Zhilenkov, Anton
    Mobayen, Saleh
    SYMMETRY-BASEL, 2024, 16 (12):
  • [50] Evaluation of support vector machine and artificial neural networks in weed detection using shape features
    Bakhshipour, Adel
    Jafari, Abdolabbas
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 145 : 153 - 160