Possibility Theory-Based Approach to Spam Email Detection

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    5 Citations (Scopus)

    Abstract

    Most of current spam email detection systems use keywords in a blacklist to detect spam emails. However these keywords can be written as misspellings, for example "baank", "ba-nk" and "bankk" instead of "bank". Moreover, misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing such misspellings. However it is impossible to predict all possible misspellings for a given keyword to add those to the blacklist. We present a possibility theory-based approach to spam email detection to solve this problem. We consider every keyword in the blacklist along with its misspellings as a fuzzy set and propose a possibility function. This function will be used to calculate a possibility score for an unknown email. Using a proposed if-then rule and this core, we can decide whether or not this unknown email is spam. Experimental results are also presented
    Original languageEnglish
    Title of host publication2007 IEEE International Conference on Granular Computing (GRC 2007)
    EditorsT.Y Lin, X Hu
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages571-575
    Number of pages5
    ISBN (Print)9780769530321
    DOIs
    Publication statusPublished - 2007
    EventIEEE International Conference on Granular Computing - San Jose, United States
    Duration: 2 Nov 20074 Nov 2007

    Conference

    ConferenceIEEE International Conference on Granular Computing
    CountryUnited States
    CitySan Jose
    Period2/11/074/11/07

    Fingerprint

    Electronic mail
    Fuzzy sets

    Cite this

    Tran, D., Ma, W., Sharma, D., & Nguyen, T. (2007). Possibility Theory-Based Approach to Spam Email Detection. In T. Y. Lin, & X. Hu (Eds.), 2007 IEEE International Conference on Granular Computing (GRC 2007) (pp. 571-575). United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/GrC.2007.123
    Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra ; Nguyen, Thien. / Possibility Theory-Based Approach to Spam Email Detection. 2007 IEEE International Conference on Granular Computing (GRC 2007). editor / T.Y Lin ; X Hu. United States : IEEE, Institute of Electrical and Electronics Engineers, 2007. pp. 571-575
    @inproceedings{12150ebdaa6c41dcbc8bfc17770d0915,
    title = "Possibility Theory-Based Approach to Spam Email Detection",
    abstract = "Most of current spam email detection systems use keywords in a blacklist to detect spam emails. However these keywords can be written as misspellings, for example {"}baank{"}, {"}ba-nk{"} and {"}bankk{"} instead of {"}bank{"}. Moreover, misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing such misspellings. However it is impossible to predict all possible misspellings for a given keyword to add those to the blacklist. We present a possibility theory-based approach to spam email detection to solve this problem. We consider every keyword in the blacklist along with its misspellings as a fuzzy set and propose a possibility function. This function will be used to calculate a possibility score for an unknown email. Using a proposed if-then rule and this core, we can decide whether or not this unknown email is spam. Experimental results are also presented",
    author = "Dat Tran and Wanli Ma and Dharmendra Sharma and Thien Nguyen",
    year = "2007",
    doi = "10.1109/GrC.2007.123",
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    publisher = "IEEE, Institute of Electrical and Electronics Engineers",
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    Tran, D, Ma, W, Sharma, D & Nguyen, T 2007, Possibility Theory-Based Approach to Spam Email Detection. in TY Lin & X Hu (eds), 2007 IEEE International Conference on Granular Computing (GRC 2007). IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 571-575, IEEE International Conference on Granular Computing, San Jose, United States, 2/11/07. https://doi.org/10.1109/GrC.2007.123

    Possibility Theory-Based Approach to Spam Email Detection. / Tran, Dat; Ma, Wanli; Sharma, Dharmendra; Nguyen, Thien.

    2007 IEEE International Conference on Granular Computing (GRC 2007). ed. / T.Y Lin; X Hu. United States : IEEE, Institute of Electrical and Electronics Engineers, 2007. p. 571-575.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    T1 - Possibility Theory-Based Approach to Spam Email Detection

    AU - Tran, Dat

    AU - Ma, Wanli

    AU - Sharma, Dharmendra

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    N2 - Most of current spam email detection systems use keywords in a blacklist to detect spam emails. However these keywords can be written as misspellings, for example "baank", "ba-nk" and "bankk" instead of "bank". Moreover, misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing such misspellings. However it is impossible to predict all possible misspellings for a given keyword to add those to the blacklist. We present a possibility theory-based approach to spam email detection to solve this problem. We consider every keyword in the blacklist along with its misspellings as a fuzzy set and propose a possibility function. This function will be used to calculate a possibility score for an unknown email. Using a proposed if-then rule and this core, we can decide whether or not this unknown email is spam. Experimental results are also presented

    AB - Most of current spam email detection systems use keywords in a blacklist to detect spam emails. However these keywords can be written as misspellings, for example "baank", "ba-nk" and "bankk" instead of "bank". Moreover, misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing such misspellings. However it is impossible to predict all possible misspellings for a given keyword to add those to the blacklist. We present a possibility theory-based approach to spam email detection to solve this problem. We consider every keyword in the blacklist along with its misspellings as a fuzzy set and propose a possibility function. This function will be used to calculate a possibility score for an unknown email. Using a proposed if-then rule and this core, we can decide whether or not this unknown email is spam. Experimental results are also presented

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    Tran D, Ma W, Sharma D, Nguyen T. Possibility Theory-Based Approach to Spam Email Detection. In Lin TY, Hu X, editors, 2007 IEEE International Conference on Granular Computing (GRC 2007). United States: IEEE, Institute of Electrical and Electronics Engineers. 2007. p. 571-575 https://doi.org/10.1109/GrC.2007.123