# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 3 The Proposed Model
### Machine Learning Modeling
#### 3.3.2 Deep Learning
## 5 Conclusions

In this paper, a hybrid model that is based on CNN and LSTM is presented for classifying SMS spam, which models SMS contexts (such as mobile network messages, Facebook messenger messages, WhatsApp messages). For the evaluation dataset, a set of messages in Arabic and English is collected in order to obtain a real dataset. Support vector machine (SVM), K-Nearest Neighbors (KNN), Multinomial Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), AdaBoost (AB), Bagging classifier, and Extra Trees are also utilized in order to classify SMS spam based on collected data. The experimental evaluation of the proposed approach has shown that the CNN-LSTM model performs better than other techniques for classifying SMS spam. The experimental results showed that our CNN-LSTM model achieved an accuracy of 98.37%, a precision of 95.39%, a recall of 87.87%, an F1-Score of 91.48%, and an AUC of 93.7%. This solution can significantly improve the security of smartphones by filtering spam messages and minimizing the risks that are related to smishing attacks in mobile environments. As future work, we plan to create a rich framework capable of filtering spam messages in smartphones with better precision. The objective is to add more functionalities, such as the analysis of URLs or files attached to messages and the inspection of telephone numbers included in messages. Figure 10: ROC curve of CNN-LSTM model. Figure 9: Confusion Matrix of CNN-LSTM model. **Author Contributions:** Conceptualization, A.G. and M.A.M. ; Methodology, A.G.; Software, A.G. and Q.M.A. ; Validation, A.G., M.A.M. and Q.M.A. ; Resources, M.A.M. ; Data curation, A.G.; Formal analysis, A.G.; Investigation, A.G.; Project administration, A.G.; Supervision, A.G.; Visualization, A.G.; Writing--original draft, A.G. and M.A.M. ; Writing--review & editing, A.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

* [PERSON] (2017) [PERSON] Daily SMS Mobile Usage Statistics. 2017. Available online: [[https://www.smseagle.eu/2017/03/06/daily-sms-mobile-statistics/](https://www.smseagle.eu/2017/03/06/daily-sms-mobile-statistics/)]([https://www.smseagle.eu/2017/03/06/daily-sms-mobile-statistics/](https://www.smseagle.eu/2017/03/06/daily-sms-mobile-statistics/)) (accessed on 15 June 2020). * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON] Deep learning to filter SMS Spam. _Future Gener. Comput. Syst._**2020**, _102_, 524-533. [CrossRef]
* [PERSON] (2011) Tatango. Text Message Spam Infographic. 2011. Available online: [[https://www.tatango.com/blog/text-message-spam-infographic/](https://www.tatango.com/blog/text-message-spam-infographic/)]([https://www.tatango.com/blog/text-message-spam-infographic/](https://www.tatango.com/blog/text-message-spam-infographic/)) (accessed on 15 June 2020). * [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] [PERSON] Smishing-Classifier: A Novel Framework for Detection of Smishing Attack in Mobile Environment. In Proceedings of the Smart and Innovative Trends in Next Generation Computing Technologies (NGCT 2017), Dehradun, India, 30-31 October 2017; pp. 502-512. * [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] Mobile phishing attacks and defence mechanisms: State of art and open research challenges. _Comput. Secur._**2018**, _73_, 519-544. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques. _IJESMA_**2020**, _12_, 21-38. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Smishing Detector: A security model to detect smishing through SMS content analysis and URL behavior analysis. _Future Gener. Comput. Syst._**2020**, _108_, 803-815. [CrossRef]
* [PERSON] (2012) [PERSON] Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networks. In _Guide to OCR for Arabic Scripts_; Springer: London, UK, 2012, pp.