Determining of The User Attitudes on Mobile Security Programs with Machine Learning Methods

Rıdvan Yayla, Turgay Tugay Bilgin

Abstract


Security plays an important role in today's virtual world. Cybersecurity software has widely been used by the development of portable virtual environments. Smartphones take place in an important part of our lives. Daily routines are carried out over mobile phones, especially after the Covid-19 pandemic process. Due to its ease of use, compulsory or optional mobile phone use brought also about a lot of security concerns. Mobile security software is used for different purposes such as virus removal and protection of personal information according to user preferences. In the field of natural language processing, user preferences can now be analyzed on the basis of machine learning methods with sentiment analysis. In this paper, the preference reasons for mobile security software are analyzed with machine learning methods based on user comments and sentiment analysis. In the study, all user comments were classified into 10 main categories and the user preferences of mobile security programs were analyzed.


Full Text:

PDF

References


N. Manochehri and M. Y. Alhinai (2006). "Mobile phone users attitude towards Mobile Commerce (m-commerce) and Mobile Services in Oman". 2nd IEEE/IFIP International Conference in Central Asia on Internet, Tashkent, Uzbekistan, pp. 1-6, https://doi.org/10.1109/CANET.2006.279277.

V. Tambe, D. Chauhan, S. Kulal and S. Sherkhane (2018). "Offline Mobile Security". 2018 International Conference on Smart City and Emerging Technology (ICSCET), IEEE, Mumbai, India, pp.1-4, https://doi.org/10.1109/ICSCET.2018.8537303.

N. Clarke, J. Symes, H. Saevanee and S. Furnell, S. (2016). “Awareness of Mobile Device Security: A Survey of User's Attitudes”. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), IGI Global Publisher of Timely Knowledge7(1), pp.15-31. https://doi.org/10.4018/IJMCMC.2016010102

C. Ozkan and K. Bicakci (2020). "Security Analysis of Mobile Authenticator Applications," 2020 International Conference on Information Security and Cryptology (ISCTURKEY), IEEE, Ankara, Turkey,pp.18-30, https://doi.org/10.1109/ISCTURKEY51113.2020.9308020.

J. Ophoff and M. Robinson (2014). "Exploring end-user smartphone security awareness within a South African context," 2014 Information Security for South Africa, IEEE, Johannesburg, South Africa, pp. 1-7, https://doi.org/10.1109/ISSA.2014.6950500.

Z. Zhou, C. Sun, J. Lu and F. Lv (2018). "Research and Implementation of Mobile Application Security Detection Combining Static and Dynamic", 2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), IEEE, Changsha, China, pp. 243-247, https://doi.org/10.1109/ICMTMA.2018.00065.

Z. Benenson, O. Kroll-Peters and M. Krupp (2012). "Attitudes to IT security when using a smartphone," 2012 Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE,Wroclaw, Poland, pp. 1179-1183.

S. Ray (2017)., "Understanding Support Vector Machine (SVM) algorithm from examples", Analytic Vidhya, Retrieved from https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/

N. S. Huda, M. S. Mubarok and Adiwijaya (2019)."A Multi-label Classification on Topics of Quranic Verses (English Translation) Using Backpropagation Neural Network with StochasticGradient Descent and Adam Optimizer," 2019 7th International Conference on Information and Communication Technology (ICoICT), IEEE, Kuala Lumpur, Malaysia, pp. 1-5, https://doi.org/10.1109/ICoICT.2019.8835362.

M. S. Alsadi, R. Ghnemat and A. Awajan (2019)."Accelerating Stochastic Gradient Descent using Adaptive Mini-Batch Size," 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), IEEE, Amman, Jordan, pp. 1-7, https://doi.org/10.1109/ICTCS.2019.8923046.

F. Kabir, S. Siddique, M. R. A. Kotwal and M. N. Huda (2015). "Bangla text document categorization using Stochastic Gradient Descent (SGD) classifier", 2015 International Conference on Cognitive Computing and Information Processing (CCIP), IEEE, Noida, India, pp. 1-4, https://doi.org/10.1109/CCIP.2015.7100687.

A. Tripathy, A. Agrawal and S. Kumar Rath (2016). "Classification of sentiment reviews using n-gram machine learning approach", Expert Systems with Applications, Pages 117-126, https://doi.org/10.1016/j.eswa.2016.03.028.

I. N. Dewi, R. Nurcahyo and Farizal (2020). "Word Cloud Result of Mobile Payment User Review in Indonesia," 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA), Bangkok, Thailand, pp. 989-992, https://doi.org/10.1109/ICIEA49774.2020.9102048.

M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani and R. Budiarto (2020). "Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking," IEEE Access, vol. 8, pp. 90847-90861, https://doi.org/10.1109/ACCESS.2020.2994222.

C. Liu, Y. Sheng, Z. Wei and Y. Yang (2018). "Research of Text Classification Based on Improved TF-IDF Algorithm," 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), IEEE, Lanzhou, China, pp. 218-222, https://doi.org/10.1109/IRCE.2018.8492945.

V. Sundaram, S. Ahmed, S. A. Muqtadeer and R. Ravinder Reddy (2021). "Emotion Analysis in Text using TF-IDF," 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) IEEE, Noida, India, pp. 292-297, https://doi.org/10.1109/Confluence51648.2021.9377159.

T. Hasan, A. Matin and M. S. R. Joy (2020). "Machine Learning Based Automatic Classification of Customer Sentiment", 2020 23rd International Conference on Computer and Information Technology (ICCIT) IEEE, Dhaka, Bangladesh, pp.1-6, https://doi.org/10.1109/ICCIT51783.2020.9392652.

M. Kumar Jain, D. Gopalani, Y. Kumar Meena and R. Kumar (2020). "Machine Learning based Fake News Detection using linguistic features and word vector features," 2020 IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) IEEE, Prayagraj, India, pp. 1-6, https://doi.org/10.1109/UPCON50219.2020.9376576.

Ne-Lexa (2020, March 8). google-play-scraper, https://github.com/Ne-Lexa/google-play-scraper

S. M. Jimenez Zafra, M. T. Martin Valdivia, E. Martinez Camara and L. A. Urena Lopez (2019). "Studying the Scope of Negation for Spanish Sentiment Analysis on Twitter," IEEE Transactions on Affective Computing, 10(1), pp. 129-141, https://doi.org/10.1109/TAFFC.2017.2693968.

M. Rumelli, D. Akkuş, Ö. Kart and Z. Isik (2019). "Sentiment Analysis in Turkish Text with Machine Learning Algorithms," 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), IEEE, Izmir, Turkey, pp. 1-5, https://doi.org/10.1109/ASYU48272.2019.8946436.

T. T. Zin, "Sentiment Polarity in Translation" (2020). 2020 IEEE Conference on Computer Applications (ICCA) IEEE, Yangon, Myanmar, pp. 1-6, https://doi.org/10.1109/ICCA49400.2020.9022831.

Fang, X., Zhan, J. (2015). “Sentiment analysis using product review data”. Journal of Big Data vol,2, 5 pp. 1-14. https://doi.org/10.1186/s40537-015-0015-2

F. Calefato, F. Lanubile, F.Maiorano, et al. (2018). “Sentiment Polarity Detection for Software Development”. Empir Software Eng, Springer Link, 23, pp. 1352–1382 . https://doi.org/10.1007/s10664-017-9546-9




DOI: https://doi.org/10.31449/inf.v45i3.3506

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.