A Novel optimal Email Feature Selection Protocol (OEFS) for Detecting Spam Emails
Keywords:
Optimal Feature, Normalization, Score Assignment, Spam EmailAbstract
In this paper, we propose a hybrid rule-based approach, named as Optimal Email Feature Selection (OEFS) Protocol for selecting optimal features to reduce the searching time in detecting spam emails. The OEFS protocol performs email spam detection in four stages: Feature Selection, Normalization of selected features, Rank Assignment and Optimal Feature Selection. The OEFS protocol has been executed and designed for large data amount of data by achieving accurate feature generation. The performance of OEFS analyzed using different protocols in existing systems. The protocol defines here an optimality for email spam detection and correction which provides an optimal solution and outperforms all email filtering protocols like PEP and CRVSM.
References
[1] Hiroshi O., Hiromi A., Masato K., “Feature selection with a measure of deviations from Poisson in text categorization”, Expert Syst. with Appln., Pages 6826–6832., Vol. 36, Issue: 3, Apr 1, 2009
[2] Jieming Y., Yuanning L., Xiaodong Z., Zhen L., Xiaoxu Z., “A new feature selection based on comprehensive measurement both in inter-category and intra-category for text categorization”, Inf. Processing & Mgmt., Vol. 48, Issue 4, Pages 741-754, 2012
[3] Jingle, D.J. and Rajsingh, E.B.,“ColShield: an effective and collaborative protection shield for the detection and prevention of collaborative flooding of DDoS attacks in wireless mesh networks,” Human-centric Comp. and Inf. Sci., Springer Publications, Vol. 4, Issue 8, 2014
[4] Jingle, D.J., Rajsingh,E.B. and Paul, M., “Distributed Detection of DoS Using Clock Values in Wireless Broadband Networks,” Int. J. of Engg. and Advanced Tech. (IJEAT), Vol. 1, Issue 5, June 2012.
[5] Jingle, D.J and Rajsingh, E.B., “DDDOST: Distributed detection of DOS attack using timers in wireless broadband networks,” Int. Conf. on Advanced Comp. (ICoAC), IEEE, ISSN : 2377-6927, DOI : 10.1109/ICoAC.2012.6416795, 2012.
[6] Jingle, D.J. and Rajsingh,E.B., “Defending IP Spoofing Attack and TCP SYN Flooding Attack in Next Generation Multi-hop Wireless Networks,” Inter. J. of Inf. and Net. Sec. (IJINS), Vol. 2, Issue 2, Dec 2012.
[7] Paul, M. and Ravi R., “A Collaborative Reputation-based Vector Space Model for Email Spam Filtering”, J. of Comput. and Theoret. Nanosci., Vol. 15, No.2, Pages 474-479, February 2018, doi.org/10.1166/jctn.2018.7128, American Scientific Publishers, 2018
[8] P. Mano Paul, Dr. R. Ravi, “A novel Email Spam Detection protocol for next generation networks” Taga J. of Graphic Tech., Tech. Ass. of the Graphic Arts, Vol.14, Swansea Printing Technology Ltd, Pages 124-133, 2018
[9] P. Mano Paul and R. Ravi, “Cooperative Vector Based Reactive System For Protecting Email Against Spammers In Wireless Networks”, J. of Elec. Engg., Vol.18, Edition:4, ISSN 1582-4594, Dec. 2018.
[10] Tu Ouyang, Soumya Ray, Mark Allman, Michael Rabinovich, “A large-scale empirical analysis of email spam detection through network characteristics in a stand-alone enterprise”, Comp. Net., Vol. 59, 11 Feb. 2014, Pages 101-121, Elsev. B.V, http://dx.doi.org/10.1016/j.comnet.2013.08.031, 2013
[11] Vitor Basto-Fernandes, Iryna Yevseyeva, José R. Méndez, Jiaqi Zhaod, Florentino Fdez-Riverola, Michael T.M. Emmerich, “A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification”, Appl. Soft Comp., Vol. 48, Pages 111-123, 2016
[12] Wazir Zada Khan, Muhammad Khurram Khan, Fahad Bin Muhaya, Muhammad Y Aalsalem and Han-Chieh Chao, “A Comprehensive Study of Email Spam Botnet Detection”, IEEE Comm. Surv. & Tut..
[13] Youwei Wang, Yuanning Liu, Lizhou Feng, Xiaodong Zhu, “Novel feature selection method based on harmony search for email Classification”, Knowl. - Based Syst., Vol. 73, Pages 311 - 323, http://dx.doi.org/10.1016/j.knosys.2014.10.013, Elsevier B.V., January 2015
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