An Empirical Study on Mobile Security Awareness and Its Implications for Course Recommendation Systems in Education

Abstract

Contemporary society is increasingly reliant on smartphones, which have evolved into indispensable, multifunctional hubs for global connectivity, commerce, education and social interaction. While this dependence enhances productivity and access to services, it simultaneously exposes users to cyber security risks, including hacking, data theft and financial fraud. This study investigates the public perception of these risks, revealing high levels of awareness and concern but identifying a clear gap in the consistent adoption of advanced protection measures such as system updates, antivirus software and VPNs. The findings further highlight a strong willingness among users to invest in more effective security solutions, indicating the need for user-friendly, AI-driven protective mechanisms. Beyond general applications, these insights are particularly relevant for educational platforms such as course recommendation systems for students, where the protection of sensitive academic and personal data is critical to ensuring trust, adoption, and effective learning outcomes.

Introduction

• Smartphones have become essential tools in modern life, revolutionizing communication, business, education, and entertainment.

• Educational systems, like Course Recommendation Systems (CRS), use smartphones to deliver personalized academic content.

• However, the widespread use of smartphones brings serious cybersecurity risks, including hacking, data theft, and malware attacks.

?? Security Threats and Challenges

• Smartphones store sensitive information (e.g., banking data, student records, personal messages), making them targets for cybercriminals.

• Key threats include:

  • Phishing

  • Public Wi-Fi attacks

  • Malware & spyware

  • SIM swapping

  • Bluetooth exploits

  • Device rooting/jailbreaking

• Despite having built-in features like biometric locks and digital assistants, vulnerabilities persist due to constant connectivity and use in unsecured environments.

???? Security in Educational Applications

• CRS systems rely on mobile platforms and are vulnerable to the same security threats.

• A breach in CRS can compromise student data and disrupt learning outcomes.

• Strong security practices like multi-factor authentication, encrypted data storage, and frequent updates are essential for protecting educational platforms.

???? Research Focus and Methodology

• Objective: Assess public awareness and behavior concerning smartphone security threats and evaluate their use of protective features.

• Method:

  • Cross-sectional survey using Google Forms.

  • Sample size: 154 participants from diverse backgrounds (students, professionals, general public).

  • Descriptive statistics used to analyze responses.

???? Key Findings (Results & Discussion)

1. Awareness of Smartphone Hacking

• 78.6% were aware smartphones can be hacked.

• 21.4% were unaware — highlighting room for awareness improvement.

2. Perceived Risks of Hacking

• Top concerns:

  • Financial fraud (51.3%)

  • Data theft (33.1%)

  • Others: surveillance, device damage, identity theft

3. Knowledge of Hacking Methods

• Most recognized:

  • Phishing (66.2%)

  • Public Wi-Fi attacks (55.8%)

  • Malware (47.4%)

• Least recognized:

  • Jailbreaking/rooting (16.9%)

  • Bluetooth exploits (22.1%)

4. Experience with Hacking

• 50% of respondents had experienced or knew someone who had experienced smartphone hacking.

5. Use of Security Features

• Most used:

  • Screen lock: 78.6%

  • 2FA (two-factor authentication): 55.8%

• Less used:

  • Antivirus apps: 39.6%

  • System updates: 32.5%

  • VPNs for public Wi-Fi: 16.9%

6. Update Practices

• 59.1% update regularly.

• 40.9% update occasionally, rarely, or never—creating security vulnerabilities.

7. App Download Sources

• 90.3% download only from official app stores.

• 9.7% still use unofficial sources, increasing malware risk.

???? Study Objectives Recap

1. Evaluate public awareness of smartphone hacking.

2. Identify the gap between awareness and adoption of security practices.

3. Understand perceptions of effective security solutions.

4. Stress the importance of mobile security in education platforms like CRS.

Conclusion

Based on the survey, the general conclusion is that there is widespread awareness and concern about smartphone hacking, supported by significant direct and indirect experience with security incidents. While most users adopt basic measures such as screen locks and official app downloads, the consistent use of advanced protections like regular system updates, antivirus software, and VPNs remains limited. This gap underscores the need for better education and more accessible security solutions. Importantly, the implications extend beyond everyday mobile use to domain-specific applications such as course recommendation systems for students. These systems, which depend on sensitive academic records and personal data, require equally strong mobile security practices to ensure both privacy and trust in their recommendations. Thus, the integration of robust, user-friendly, and AI-driven security measures is essential not only for personal device protection but also for supporting secure, data-driven educational platforms.

References

[1] Sonali Saxena, “Unveiling Intrusions: Advanced Methods For Detecting Mobile Phone Hacking,” TIJER International Research Journal, Volume 12, Issue 2, a146-163,February 2025. [2] Koppula Venkata Satya, Penugonda Praneeth Reddy, Dr. Manikandan K, “Study on Modern Methods for Detecting Mobile Malware,” International Research Journal of Engineering and Technology, Volume 9, Issue 9, 724-730, September 2022. [3] Ogundele Israel Oludayo, Akinwole Agnes Kikelomo, Adebayo Adeniran Adedeji, Aromolaran Adewale Ayodeji, “A Review of Smartphone Security Challenges and Prevention,” International Research Journal of Innovations in Engineering and Technology, Volume 7, Issue 5, 234-245, May 2023. [4] Amita G Chin, Philip Little, Beth H Jones, “An Analysis of Smartphone Security Practices among Undergraduate Business Students at a Regional Public University,” International Journal of Education and Development using Information and Communication Technology, Volume 16, Issue 1,44-61, 2020. [5] Loreen M. Powell, Jessica Swartz, Michalina Hendon, “Awareness of mobile device security and data privacy tools,” Issues in Information Systems, Volume 22, Issue 1, 1-9, 2021. [6] Ansh Singh, Gulshan Kumar, “A Research Paper on Cyber Security,” International Journal of Research Publication and Reviews, Volume 5, Issue 4, 867-871, April 2024. [7] Himanshu Shewale, Sameer Patil, Vaibhav Deshmukh, Pragya Singh, “Analysis of Android Vulnerabilities and Modern Exploitation Techniques,” ICTACT Journal on Communication Technology, Volume 5, Issue 1, 863-867, March 2014. [8] Mohd Shamshul Anuar Omar, Mohamad Fadli Zolkipli, “Fundamental Study of Hacking Attacks Protection using Artificial intelligence (AI),” Volume 5, Issue 2, 813-821, February 2023. [9] Rakesh Kumar, “A study on attack and security in wireless Smartphone communication systems,” International Journal of Enhanced Research in Science, Technology & Engineering, Volume 12, Issue 8, 100-109, August 2023. [10] Anil Sopan Kalokhe, Vijaykumar Sambhajirao Kumbhar, “A Review on Course Recommendation System in Higher Education using Machine Learning,” International Research Journal of Humanities and Interdisciplinary Studies, 47-55, January 2024. [11] C. Saroja, Shaik Haseena , B. Keerthana, P. Sukanya, P. HemaMalini, “Online Course Recommendation System Using Machine Learning,” International Journal of Scientific Research & Engineering Trends, Volume 11, Issue 2, 2531-2535, March-April 2025. [12] Kalokhe Anil Sopan, Kumbhar Vijaykumar Sambhajirao, “Intelligent Course Recommendation for Students using Machine Learning Models,” International Journal of All Research Education and Scientific Methods, Volume 12, Issue 12, 2397-2402, December 2024. [13] Wangmei Chen, Zepeng Shen, Yiming Pan, Kai Tan, Cankun Wang, “Applying Machine Learning Algorithm to Optimize Personalized Education Recommendation System,” Journal of Theory and Practice of Engineering Science, Volume 4, Issue 1, 101-108, 2024.

Copyright

Copyright © 2025 Kalokhe Anil Sopan, Shinde Gauri Krushnath, Kharade Vaishnavi Santosh, Kumbhar Vijaykumar Sambhajirao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.