Abstract
With the increase in popularity of smartphones, text-based communication has also gained popularity. Availability of messaging services at low cost has resulted into the increase in spam messages. This increase in number of spam messages has become an important issue these days. Many mobile applications are developed to detect spam messages in mobile phones but still, there is a lack of a complete solution. This paper presents an approach for the detection of spam messages. We have identified an effective feature set for text messages which classify the messages into spam or ham with high accuracy. The feature selection procedure is implemented on normalized text messages to obtain a feature vector for each message. The feature vector obtained is tested on a set of machine learning algorithms to observe their efficiency. This paper also presents a comparative analysis of different algorithms on which the features are implemented. In addition, it presents the contribution of different features in spam detection. After implementation and as per the set of features selected, Artificial Neural Network Algorithm using Back Propagation technique works in the most efficient manner.
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Goel, D., & Jain, A. K. (2018). Mobile phishing attacks and defence mechanisms: state of art and open research challenges. Computers & Security,73, 519–544.
Gudkova, D. (2018). Spam and Phishing in 2017. Securelist—Kaspersky Lab’s Cyber threat Research and Reports, 15 Feb, 2018, http://securelist.com/spam-and-phishing-in-2017/83833/. Retrived April, 2018.
Crowe, J. (2017). Must-Know Phishing Statistics 2017. Barkly Endpoint Security Blog. http://blog.barkly.com/phishing-statistics-2017. Retrived April 2018.
Goel, D., & Jain, A. K. (2017). Smishing-classifier: A novel framework for detection of Smishing attack in mobile environment. In International conference on next generation computing technologies (pp. 502–512). Singapore: Springer.
Almeida, T. A., Silva, T. P., Santos, I., & Hidalgo, J. M. G. (2016). Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering. Knowledge-Based Systems,108, 25–32.
Joo, J. W., Moon, S. Y., Singh, S., & Park, J. H. (2017). S-detector: An enhanced security model for detecting Smishing attack for mobile computing. Telecommunication Systems,66(1), 29–38. https://doi.org/10.1007/s11235-016-0269-9.
Etaiwi, W., & Awajan, A. (2017). The effects of features selection methods on spam review detection performance. International Conference on New Trends in Computing Sciences (ICTCS). https://doi.org/10.1109/ictcs.2017.50.
Patel, R., & Thakkar, P. (2014). Opinion spam detection using feature selection. International Conference on Computational Intelligence and Communication Networks.. https://doi.org/10.1109/cicn.2014.127.
Ali, S. S., & Maqsood, J. (2018). Net library for SMS spam detection using machine learning: A cross platform solution. 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST).. https://doi.org/10.1109/ibcast.2018.8312266.
Jain, A. K., & Gupta, B. (2018). Rule-based framework for detection of Smishing messages in mobile environment. Procedia Computer Science,125, 617–623. https://doi.org/10.1016/j.procs.2017.12.079.
Wu, T., Liu, S., Zhang, J., & Xiang, Y. (2017). Twitter spam detection based on deep learning. Proceedings of the Australasian Computer Science Week Multiconference on—ACSW. https://doi.org/10.1145/3014812.3014815.
Sethi, P., Bhandari, V., & Kohli, B. (2017). SMS spam detection and comparison of various machine learning algorithms. International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN),1, 1. https://doi.org/10.1109/ic3tsn.2017.8284445.
Ma, J., Zhang, Y., Liu, J., Yu, K., & Wang, X. (2016) Intelligent SMS spam filtering using topic model. In International conference on intelligent networking and collaborative systems (INCoS) (pp 380–383). IEEE.
Gómez Hidalgo, J. M., Bringas, G. C., Sánz, E. P., & García, F. C. (2006). Content based SMS spam filtering. In ACM symposium on Document engineering (pp. 107–114). ACM.
Feng, W., Sun, J., Zhang, L., Cao, C., & Yang, Q. (2016). A support vector machine based naive Bayes algorithm for spam filtering. In 35th International conference on performance computing and communications conference (IPCCC) (pp. 1–8). IEEE.
FreeLing. (2018). http://devel.cpl.upc.edu/freeling/. Retrived April 2018.
Internet & Text Slang Dictionary, www.noslang.com/dictionary/. Retrived April, 2018.
Gupta, S., & Singhal, A. (2017). Phishing URL detection by using artificial neural network with PSO. In 2nd International Conference on Telecommunication and Networks (TEL-NET) (pp. 1–6). IEEE.
SMS Spam Collection. http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/. Retrived April, 2018.
Grumbletext UK Forum. http://www.grumbletext.co.uk/. Retrived April, 2018.
A corpus linguistic study of SMS Text Messaging. http://etheses.bham.ac.uk/253/1/Tagg09PhD.pdf. Retrived April, 2018.
NUS Natural Language Processing Group. http://www.comp.nus.edu.sg/~rpnlpir/downloads/corpora/smsCorpus/. Retrived April, 2018.
Frank, E., Hall, M. A., & Witten, I. H. (2016). The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques” (4th ed.). Burlington: Morgan Kaufmann.
Chi, X., Siew, T. P., & Cambria, E. (2017). Adaptive two-stage feature selection for sentiment classification. IEEE International Conference on Systems, Man, and Cybernetics (SMC). https://doi.org/10.1109/smc.2017.8122782.
Etaiwi, W., & Awajan, A. (2017). The effects of features selection methods on spam review detection performance. International Conference on New Trends in Computing Sciences (ICTCS).,1, 1. https://doi.org/10.1109/ictcs.2017.50.
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Jain, A.K., Goel, D., Agarwal, S. et al. Predicting Spam Messages Using Back Propagation Neural Network. Wireless Pers Commun 110, 403–422 (2020). https://doi.org/10.1007/s11277-019-06734-y
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DOI: https://doi.org/10.1007/s11277-019-06734-y