Security in Mobile Network: Issues, Challenges and Solutions
DOI:
https://doi.org/10.4108/eetiot.4542Keywords:
mobile attacks, mobile security, data privacy, mobile applications, malware attacksAbstract
INTRODUCTION: Mobile devices are integrated into daily activities of people's life. Compared to desktop computers the growth of mobile devices is tremendous in recent years. The growth of mobile devices opens vast scope for attackers on these devices.
OBJECTIVES: This paper presents a deep study of different types of security risks involved in mobile devices and mobile applications.
METHODS: In this paper we study various mechanisms of security risks for the mobile devices and their applications. We also study how to prevent these security risks in mobile devices.
RESULTS: Various solutions are provided in paper through which operators can protect the security and privacy of user data and keep their customers' trust by implementing these procedures.
CONCLUSION: This paper concludes with their solutions for providing a secure mobile network. This paper is structured as follows. Section 2 contains related work. Section 3 describes security problems. Section 4 discusses defensive methods and Section 5 gives the conclusion.
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References
Khan, J., Abbas, H., & Al-Muhtadi, J. (2015). Survey on Mobile User's Data Privacy Threats and Defense Mechanisms. Procedia Computer Science, 56, 376- 383.
Shukla, V., Chaturvedi, A., & Srivastava, N. (2015). A new secure authenticated key agreement scheme for wireless (mobile) communication in an EHR system using cryptography. Communication on applied electronics (CAE), 3(3), 16-21
Cifuentes, Y., Beltrán, L., & Ramírez, L. (2015, August). Analysis of Security Vulnerabilities for Mobile Health Applications. In 2015 Seventh International Conference on Mobile Computing and Networking (ICMCN 2015).
Chatzikonstantinou, A., Ntantogian, C., Karopoulos, G., & Xenakis, C. (2016, May). Evaluation of Cryptography Usage in Android Applications. In proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies, pp. 83-90.
Flauzac, O.; Nolot, F.; Rabat, C.; Steffenel, L.-A., "Grid of Security: A New Approach of the Network Security", In Proc. of Int. Conf. on Network and System Security, 2009. NSS '09, pp. 67-72, 2009.
Wu Kehe; Zhang Tong; Li Wei; Ma Gang, "Security Model Based on Network Business Security", In Proc. of Int. Conf. on Computer Technology and Development, 2009. ICCTD '09, Vol. 1, pp. 577-580, 2009.
Dowd, P.W.; McHenry, J.T., "Network security: it's time to take it seriously," Computer, vol.31, no.9, pp.24?28, Sep 1998
Kartalopoulos, S. V., "Differentiating Data Security and Network Security," Communications, 2008. ICC '08. IEEE International Conference on, pp.1469?1473, 19?23 May 2008
P. Vinod, R. Jaipur, V. Laxmi, and M. Gaur, "Survey on malware detection methods," in Proceedings of the 3rd Hackers' Workshop on the computer and internet security (IITKHACK'09), 2009, pp. 74-79.
Voor, H.G., Klievink, A.J., Arnaboldi, M., Meijerc, A.J. ?Rationality and politics of algorithms. Will the promise of big data survive the dynamics of public decision making?, Government Information Quarterly, 2019, Vol. 36(1), pp. 27-38. DOI: 10.1016/j.giq.2018.10.011
Agarwal N., Jain A., Gupta A., Tayal D.K. (2022) Applying XGBoost Machine Learning Model to Succor Astronomers Detect Exoplanets in Distant Galaxies. In: Dev A., Agrawal S.S., Sharma A. (eds) Artificial Intelligence and Speech Technology. AIST 2021. Communications in Computer and Information Science, vol 1546. Springer, Cham. https://doi.org/10.1007/978-3-030-95711-7_33
Agarwal, N., Srivastava, R., Srivastava, P., Sandhu, J., Singh, Pratap P. Multiclass Classification of Different Glass Types using Random Forest Classifier. 6th International Conference on Intelligent Computing and Control Systems (ICICCS), 2022. p. 1682-1689.
Agarwal, N., Singh, V., Singh, P. Semi-Supervised Learning with GANs for Melanoma Detection. 6th International Conference on Intelligent Computing and Control Systems (ICICCS), 2022. p. 141-147.
Tayal, D.K., Agarwal, N., Jha, A., Deepakshi, Abrol, V. To Predict the Fire Outbreak in Australia using Historical Database. 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2022. p. 1-7.
Agarwal, N., Tayal, D.K. FFT based ensembled model to predict ranks of higher educational institutions. Multimed Tools Appl 81, 2022.
Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6
Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470
G. P. Rout and S. N. Mohanty, "A Hybrid Approach for Network Intrusion Detection," 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, India, 2015, pp. 614-617, doi: 10.1109/CSNT.2015.76.
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