Review Study of Hoax Email Characteristic

  • Authors

    • SY. Yuliani
    • Shahrin Sahib
    • Mohd Faizal Abdollah
    • Mohammed Nasser Al-Mhiqani
    • Aldy Rialdy Atmadja
    2018-06-20
    https://doi.org/10.14419/ijet.v7i3.2.18754
  • Hoax, Fake news, Hoax detection, Hoax detection systems
  • Hoax on email is one form of attack in the cyber world where an email account will be sent with fake news that has many goals to take advantage or raise the rating of sales of a product. A Hoax can affect many people by damaging the credibility of the image of a person or group. The phenomenon of this hoax would cause anxiety in the community and even more bad effects because of the potential for the wrong power of the news or information. In this paper we review the Hoax detection systems, Types of Hoax, and machine learning models that has been used to detect the Hoax. This work serves as a basis for further studies on Hoax detection systems.

     

     
  • References

    1. [1] The Radicati Group Inc., Email Statistics Report, 2017-2021, 44 (2017) 4.

      [2] A.B. Prasetijo, R.R. Isnanto, D. Eridani, Y.A.A. Soetrisno, M. Arfan, A. Sofwan, Hoax detection system on Indonesian news sites based on text classification using SVM and SGD, 2017 4th Int. Conf. Inf. Technol. Comput. Electr. Eng. (2017) 45–49. doi:10.1109/ICITACEE.2017.8257673.

      [3] Y.Y. Chen, S.-P. Yong, A. Ishak, Email Hoax Detection System Using Levenshtein Distance Method, J. Comput. 9 (2014) 441–446. doi:10.4304/jcp.9.2.441-446.

      [4] N. Kurniasih, N. Kurniasih, L.A. Abdillah, I.K. Sudarsana, I.W.L. Yogantara, I.N.T. Astawa, R.F. Nanuru, A. Miagina, J.O. Sabarua, M. Jamil, J. Tandisalla, E. Duan, F.G.J. Rupilele, M.D. Utama, M. Laisila, A.S. Ahmar, R. Rahim, Prototype Application Hate Speech Detection Website Using String Matching and Searching Algorithm, Int. J. Eng. Technol. 7 (2018) 62–64. doi:10.14419/ijet.v7i2.5.13952.

      [5] E. Tacchini, G. Ballarin, M.L. Della Vedova, S. Moret, L. de Alfaro, Some like it Hoax: Automated fake news detection in social networks, CEUR Workshop Proc. (2017). doi:10.1257/jep.31.2.211.

      [6] A. Elyashar, J. Bendahan, R. Puzis, Is the News Deceptive? Fake News Detection using Topic Authenticity, (2017) 16–21.

      [7] S.M. Sirajudeen, N.F.A. Azmi, A.I. Abubakar, Online fake news detection algorithm, J. Theor. Appl. Inf. Technol. 95 (2017) 4114–4122.

      [8] S. Zannettou, M. Sirivianos, J. Blackburn, N. Kourtellis, The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans, (2018) 1–26.

      [9] M. Vuković, K. Pripužić, H. Belani, An intelligent automatic hoax detection system, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 5711 LNAI (2009) 318–325. doi:10.1007/978-3-642-04595-0_39.

      [10] T. Petković, T. Petković, Z. KostanjÄar, P. Pale, E-Mail System for Automatic Hoax Recognition, (2005).

      [11] T. Heyd, Email hoaxes: Form, function, genre ecology., Email Hoaxes Form, Funct. Genre Ecol. (2008).

      [12] S.M. University, Characteristics of Viruses and Virus Hoaxes, (n.d.).

      [13] Scam emails, (n.d.).

      [14] X. Wu, V. Kumar, Q.J. Ross, J. Ghosh, Q. Yang, H. Motoda, G.J. McLachlan, A. Ng, B. Liu, P.S. Yu, Z.H. Zhou, M. Steinbach, D.J. Hand, D. Steinberg, Top 10 algorithms in data mining, 2008. doi:10.1007/s10115-007-0114-2.

      [15] W.B. Zulfikar, N. Lukman, Perbandingan Naive Bayes Classifier Dengan Nearest Neighbor Untuk Identifikasi Penyakit Mata, J. Online Inform. 1 (2016) 82–86. doi:10.15575/join.v1i2.33.

      [16] J.S. Whissell, C.L. a Clarke, Clustering for Semi-Supervised Spam Filtering Categories and Subject Descriptors, Proc. 8th Annu. Collab. Electron. Messag. Anti-Abuse Spam Conf. ACM. (2011) 125–134.

      [17] E.P. Sanz, J.M.G. Hidalgo, J.C.C. Pérez, Email Spam Filtering, Adv. Comput. 74 (2008) 45–114. doi:10.1016/S0065-2458(08)00603-7.

      [18] T.S. Guzella, W.M. Caminhas, A review of machine learning approaches to Spam filtering, Expert Syst. Appl. 36 (2009) 10206–10222. doi:10.1016/j.eswa.2009.02.037.

      [19] E. Michelakis, I. Androutsopoulos, G. Paliouras, G. Sakkis, Filtron : A Learning-Based Anti-Spam Filter . Filtron : A Learning-Based Anti-Spam Filter, in: CEAS 2004 - First Conf. Email Anti-Spam, 2004.

      [20] A. Bhowmick, S.M. Hazarika, Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends, (2016).

      [21] A.R. Atmadja, A. Purwarianti, Comparison on the rule based method and statistical based method on emotion classification for Indonesian Twitter text, 2015 Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2015 - Proc. (2016). doi:10.1109/ICITSI.2015.7437692.

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  • How to Cite

    Yuliani, S., Sahib, S., Faizal Abdollah, M., Nasser Al-Mhiqani, M., & Rialdy Atmadja, A. (2018). Review Study of Hoax Email Characteristic. International Journal of Engineering & Technology, 7(3.2), 778-782. https://doi.org/10.14419/ijet.v7i3.2.18754