Evaluation of Time Complexities of Bayesian Vs Hybridized Word Stemming Techniques for Advanced Fee Fraud Emails Filtering
Keywords:
Time execution, Classification, Spam, Mail, Word stemmingAbstract
Communication in Physical Sciences, 2020, 7(2):82-86
Authors: Okunade Oluwasogo, Adekunle, Afolorunso Adenrele and Adebayo Adegboyega
Received 18 December 2020/Accepted 20 April 2021
Time execution of content-based spam filter was investigated using the Bayesian statistical algorithm against Bayesian statistical algorithm incorporated with a word stemming. The execution time intervals for the algorithms implementation of the two techniques were evaluated by subjecting the filters to manipulated and non-manipulate spam mails. The experiment shown that both single technique (Bayesian) and combined techniques (Bayesian incorporated with word stemming) executed suspicious terms manipulated mails faster (within a short time) compared to non-manipulate suspicious terms mails. Combined algorithms performed better and faster in a sophisticated and manipulated environment. The algorithm is more rugged and performed better when suspicious term/tokens were manipulated to deceit the filter.
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References
Albercht. K, (2006). Mastering Spam: A Multifaceted Approach with the Spamato Spam Filter System DSS. ETH NO. 16839
Ali, C. & Tunga, G. (2007). Time-efficient spam e-mail filtering using n-gram models. Department of Computer Engineering, Bogazic University, Istanbul 34342, Turkey
Alireza, N. P., Raheleh, K. & Soheil, B. R. (2012). Minimizing the time of spam mail detection by relocating filtering system to the sender mail server. International Journal of Network Security & Its Applications (IJNSA), 4, 2, pp. 53-62. doi: 10.5121/ijnsa.
Almomani, A., Obeidat1, A., Alsaedi, A., Obaida, M. A. & Al-Betar, M. (2015). Spam e-mail filtering using ecos algorithms. Indian Journal of Science and Technology, 8, S9, pp. 260-272
Andrej, B., Gordon, V. C., Bogdan, F. C., Thomas, R. L. & Blaz, Z. (2006). Spam filtering using statistical data compression models. Journal of Machine Learning Research, 7, pp. 2673-2698.
Awad, W..A. &. ELseuofi, S.M. (2011). Machine learning methods for spam e-mail classification. International Journal of Computer Science & Information Technology (IJCSIT), 3, 1, doi: 10.5121/ijcsit. 3112 173.
Christina, V., Karpagavalli, S. & Suganya, G. (2010). A study on email spam filtering techniques. International Journal of Computer Applications, Applications (0975 – 8887), 12, 1, pp. 7-9 Fight Cybercrime (November, 2008) Anti-phishing Techniques SpamAlert.org
Garacia, F. D, Hoepman, J & Nieuwenhuizen, J. V. (2004). Spam filter analysis. IFIP International Information Security Conference, 74, pp. 395-410
Hall, R. J, (1996). Channels: Avoiding unwanted electronic mail. In Proceeding Symposium on Network Threats. DIMACS.
Monthy, P. (1989). Flying Circus. Just the word. Menthuem Publishing Ltd. 2, 25, pp. 27-28.
Process (2010). Bayesian filtering example using bays’ formula to keep spam out of Your Inbox. http://www.process.com/
Rekha & Sandeep, N. (2014). A Review on different spam detection approaches. International Journal of Engineering Trends and Technology (IJETT), 11, pp. 315-319.
Sanjay, K. N. (2015). Spam filtering using the social anthropology and data mining technique. International Journal of Computer Science and Mobile Computing. IJCSMC, 4, 4, pp. 234-237.
Savita, T. & Santoshkumar, B. (2014). Effective Spam Detection Method for Email. IOSR
Journal of omputer Science (IOSR-JCE). e-ISSN: 2278-061, p-ISSN: 2278-8727, pp. 68-72 www.iosrjurnals.org
Tian, X. (2020). Constant time complexity spam detection algorithm for boosting throughput on rule-based filtering systems. IEEE Access.
Zhe, W., William, J., Qin, L., Moses, C. and Kai, L. (2007). Filtering image spam with near-duplicate detection. Computer Science Department, Princeton University, USA
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