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An Enhanced Framework to Mitigate Post-Installation Cyber Attacks on Android Apps

Authors

  • Vijay Koka Department of CSE, GITAM School of Technology, Hyderabad, India https://orcid.org/0009-0001-1036-6300
  • Kireet Muppavaram Department of CSE, GITAM School of Technology, Hyderabad, India
Volume: 14 | Issue: 4 | Pages: 14788-14792 | August 2024 | https://doi.org/10.48084/etasr.7467

Abstract

The widespread use of smartphones worldwide has led to a corresponding rise in the number of mobile applications available for Android devices. These apps offer users convenient ways to perform various daily tasks, but their proliferation has also created an environment in which attackers can steal sensitive information. Insecure options employed by many app developers create vulnerabilities that can be exploited by attackers to gain access to most smartphones. While existing methods can detect malware during app installation, they do not sufficiently address post-installation attacks, such as those resulting from fake apps or Man-in-the-Disk (MitD) attacks. To address this issue, the current study conducted research on post-installation attacks, including data leakage, malware injection, repackaging, reverse engineering, privilege escalation, and UI spoofing. MitD attacks are particularly challenging to counter, so, to mitigate this risk, the Post-Installation App Detection Method is proposed to monitor and regulate sensitive information flow and prevent MitD attacks.

Keywords:

malware, attacks in android apps, post installation attacks, fake apps, MITD attacks, cyber attacks

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References

J. Kumar and G. Ranganathan, "Malware Attack Detection in Large Scale Networks using the Ensemble Deep Restricted Boltzmann Machine," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11773–11778, Oct. 2023.

M. Kireet, P. Rachala, M. S. Rao, and R. Sreerangam, "Investigation Of Contemporary Attacks In Android Apps," International Journal of Scientific & Technology Research, vol. 8, no. 12, pp. 1789–1794, 2019.

S. Nasiri, M. T. Sharabian, and M. Aajami, "Using Combined One-Time Password for Prevention of Phishing Attacks," Engineering, Technology & Applied Science Research, vol. 7, no. 6, pp. 2328–2333, Dec. 2017.

Y. Sun et al., "Detecting Malware Injection with Program-DNS Behavior," in 2020 IEEE European Symposium on Security and Privacy (EuroS&P), Genoa, Italy, Sep. 2020, pp. 552–568.

M. Conti, N. Dragoni, and V. Lesyk, "A Survey of Man In The Middle Attacks," IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 2027–2051, 2016.

M. Yaseen et al., "MARC: A Novel Framework for Detecting MITM Attacks in eHealthcare BLE Systems," Journal of Medical Systems, vol. 43, no. 11, Oct. 2019, Art. no. 324.

S. Anand and V. Perumal, "EECDH to prevent MITM attack in cloud computing," Digital Communications and Networks, vol. 5, no. 4, pp. 276–287, Nov. 2019.

S. A. Roseline, S. Geetha, S. Kadry, and Y. Nam, "Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm," IEEE Access, vol. 8, pp. 206303–206324, 2020.

K. Aldriwish, "A Deep Learning Approach for Malware and Software Piracy Threat Detection," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7757–7762, Dec. 2021.

A. Souri and R. Hosseini, "A state-of-the-art survey of malware detection approaches using data mining techniques," Human-centric Computing and Information Sciences, vol. 8, no. 1, Jan. 2018, Art. no. 3.

W. Enck et al., "TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones," ACM Transactions on Computer Systems, vol. 32, no. 2, pp. 5:1-5:29, Mar. 2014.

Y. Zhou, Z. Wang, W. Zhou, and X. Jiang, "Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets," in Proceedings of the 19th Network and Distributed System Security Symposium NDSS 2012, San Diego, CA, USA, Jan. 2012.

Y. Zhou and X. Jiang, "Dissecting Android Malware: Characterization and Evolution," in 2012 IEEE Symposium on Security and Privacy, San Francisco, CA, USA, Feb. 2012, pp. 95–109.

A. A. Alhashmi, A. M. Alashjaee, A. A. Darem, A. F. Alanazi, and R. Effghi, "An Ensemble-based Fraud Detection Model for Financial Transaction Cyber Threat Classification and Countermeasures," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12433–12439, Dec. 2023.

A. Al-Marghilani, "Comprehensive Analysis of IoT Malware Evasion Techniques," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7495–7500, Aug. 2021.

"VirusTotal - Home," Virus Total. https://www.virustotal.com/gui/home/upload.

M. İbrahim, B. Issa, and M. B. Jasser, "A Method for Automatic Android Malware Detection Based on Static Analysis and Deep Learning," IEEE Access, vol. 10, pp. 117334–117352, 2022.

Y. Zhang, S. Luo, H. Wu, and L. Pan, "Antibypassing Four-Stage Dynamic Behavior Modeling for Time-Efficient Evasive Malware Detection," IEEE Transactions on Industrial Informatics, vol. 20, no. 3, pp. 4627–4639, Mar. 2024.

P. Feng, J. Ma, C. Sun, X. Xu, and Y. Ma, "A Novel Dynamic Android Malware Detection System With Ensemble Learning," IEEE Access, vol. 6, pp. 30996–31011, 2018.

H. Lu et al., "EAODroid: Android Malware Detection Based on Enhanced API Order," Chinese Journal of Electronics, vol. 32, no. 5, pp. 1169–1178, Sep. 2023.

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How to Cite

[1]
V. Koka and K. Muppavaram, “An Enhanced Framework to Mitigate Post-Installation Cyber Attacks on Android Apps”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 4, pp. 14788–14792, Aug. 2024.

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