Machine Learning Methods in E-mail Spam Classification

• Autorzy: Świtalski Piotr , Kopówka Mateusz

Identyfikatory

DOI

10.34739/si.2019.23.04

• Języki publikacji: EN

Abstrakty

EN

Increasing number of unwanted e-mails has influence on users’ security in the Internet. Today spam e-mails can store potential malicious messages which e.g. can redirect user to fake sites. These messages recently appeared in social media. Filtering of this content is important due to minimize financial and branding costs. Traditional methods of spam filtering cannot be sufficient for present threats. We required new methods for constructing more dependable and robust antispam filters. Machine learning recently becomes very popular technique in classification methods. It has been successfully used in spam classification. In this paper we present some methods of machine learning for spam detecting. We would also like to introduce ways to solve the spam classification problem. We show that these methods can be useful in classification of malicious messages. We also compared developed methods and presented results in the experimental section.

Słowa kluczowe

EN

spam detecting; machine learning; classification methods; spam filters; malicious messages

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2019
• Tom: Vol. 1-2(23)
• Strony: 57--76
• Opis fizyczny: Bibliogr. 14 poz., rys., tab., wykr.

Twórcy

autor

Świtalski Piotr

• Siedlce University of Natural Sciences and Humanities, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland

autor

Kopówka Mateusz

• Siedlce University of Natural Sciences and Humanities, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland

Bibliografia

• [1] Ahmed F., Abulaish M.: A Generic Statistical Approach for Spam Detection in Online Social Networks, Computer Communications, Volume 36, No 10-11, pp. 1120-1129, 2013, doi.org/10.1016/j.comcom.2013.04.004.
• [2] Alkaht I.J., Al-Khatib B.: Filtering SPAM Using Several Stages Neural Networks, International Review on Computers and Software, Volume 11, No 2, pp. 123-132, 2016, doi.org/10.15866/irecos.v11i2.8269.
• [3] Balogun A. K., Jaafar A., Murad M. A. A., Ezema E.: Spam Detection Approaches and Strategies: A Phenomenon, Foundation of Computer Science, Volume 12, No 9, pp. 13-18, 2017, doi.org/10.5120/ijais2017451728.
• [4] Dada E. G., Bassi J. S., Chiroma H., Abdulhamid S. M., Adetunmbi A. O., Ajibuwa O. E.: Machine learning for email spam filtering: review, approaches and open research problems, Heliyon Volume 6, No 6, 2019, doi.org/10.1016/j.heliyon.2019.e01802.
• [5] Gangavarapu, T., Jaidhar, C.D. and Chanduka, B.: Applicability of machine learning in spam and phishing email filtering: review and approaches. Artifical Intelligence Review. 53, pp. 5019–5081 (2020), doi.org/10.1007/s10462-020-09814-9.
• [6] Harris E.: The Next Step in the Spam Control War: Greylisting, Puremagic Software, 2003 (http://projects.puremagic.com/greylisting, access date: 04.10.2020).
• [7] Jalab H. A., Subramaniam T., Taqa A. Y.: Overview of textual anti-spam filtering techniques, International Journal of Physical Sciences, Volume 5, No 12, pp. 1869-1882, 2010.
• [8] Karthika R., Visalakshi P.: A hybrid ACO based feature selection method for email spam classification, WSEAS Transaction on Computers, Volume 14, 2015.
• [9] Razi Z., Asghari S. A.: Providing an Improved Feature Extraction Method for Spam Detection Baased on Genetic Algorithm in an Immune System, Journal of Knowledge-Based Engineering and Innovation, Volume 3, No 8, 2017.
• [10] Julie J.C.H.R. and Kamachi C.: Detecting and Combating Malicious Email Syngress, Chapter 2. Types of Malicious Messages., 2015, doi.org/10.1016/B978-0-12-800110-3.00002-2.
• [11] Ndumiyana D., Magomelo M., Sakala L. Ch., Spam Detection using a Neutral Network Classifier, Online Journal of Physical and Environmental Science Research, Volume 2, pp. 28-37, 2013.
• [12] Sharma A.K., Prajapat S.K., Aslam M., A comparative study between naıve Bayes and neural network (MLP) classifier for spam email detection, International Journal of Computer Applications, 2014.
• [13] Spykerman M., Typical spam characteristics How to effectively block spam and junk mail, Red Earth Software, 2003.
• [14] Taylor B., Sender Reputation in a Larger Webmail Service, CEAS 2006 – Third Conference on Email and Anti-Spam, 2006.