Abstract
In today’s digital landscape, hackers and espionage agents are increasingly targeting Android, the world’s most prevalent mobile operating system. We introduce DeepDetector - a system based on artificial intelligence to recognize data thefts in Android. This model is based upon a large dataset comprising of clean and tainted network traffic trained using a Random Forest Classifier. DeepDetector scores high in two main areas as it achieves 82.9% accuracy for connection anomaly detection and 89.9% recall in connection anomaly detection whereas it gets 78.9% accuracy and 81.6 recall in terms of detection of under the system mounted with Raspberry Pi, automatic data collection, preparing of a dataset, training and testing of the model, as well as leak detection are ensured. In this regard, DeepDetector offers a viable way of enhancing Android user security.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mobile Operating System Market Share Worldwide | Statcounter Global Stats. Statcounter Global Stats. https://gs.statcounter.com/os-market-share/mobile/worldwide. Accessed 14 Feb 2023
Senanayake, J., Kalutarage, H., Al-Kadri, M.O.: Android mobile malware detection using machine learning: a systematic review. Electronics 10(13) (2021). https://doi.org/10.3390/electronics10131606
Hossain, M.S., Ochoa, M., Patterson, K., Boettiger, C.: Detecting and visualizing anomaly in network traffic. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 1739–1748. IEEE (2015)
Eldardiry, H., Bart, E., Liu, J., Hanley, J., Price, B., Brdiczka, O.: Multi-instance multi-label learning for identifying security risks in corporate networks. In: Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Security, pp. 135–146 (2013)
Shen, Y., Mariconti, E., Vervier, P.A., Stringhini, G.: Tiresias: predicting security events through deep learning. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 592–605 (2018)
Bon[\(u\)]klu, O., Okutan, A.: Predicting insider threat with Deep Learning. In: Proceedings of the 14th International Conference on Availability, Reliability and Security, pp. 1–10 (2019)
Almubayed, A., Hadi, A., Issa, T.B.: Detecting data exfiltration using neural networks. In: 2015 10th International Conference on Information Assurance and Security (IAS), pp. 26–31. IEEE (2015)
Li, Z., Qin, Z., Huang, K., Yang, X., Ye, S.: Intrusion detection using convolutional neural networks for representation learning. In: Liu, D., **e, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 858–866. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70139-4_87
Patel, K., Patel, P., Patel, H.: Malware detection using machine learning and deep learning. In: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 563–566. IEEE (2019)
Casas, P., Mazel, J., Owezarski, P.: Unsupervised network intrusion detection systems: detecting the unknown without knowledge. Comput. Commun. 35(7), 772–783 (2012)
Rezaei, S., Liu, X.: Deep learning for encrypted traffic classification: an overview. IEEE Commun. Mag. 57(5), 76–81 (2019)
Aljawarneh, S., Aldwairi, M., Yassein, M.B.: Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J. Comput. Sci. 25, 152–160 (2018)
Hoang, X.D., Choi, J.: A novel approach for Android malware detection using deep learning. In: 2016 18th International Conference on Advanced Communication Technology (ICACT), pp. 84–89. IEEE (2016)
Ryu, J.H., Baek, K., Hwang, J., Kim, P.J.: Detecting data exfiltration from the insider threat using threat tagging and nested context. Symmetry 10(1), 22 (2018)
Cai, H., Sanfilippo, A., Glynn, E., Rathbun, L.C.: Insider threat detection by ontology-based semantic analysis of user behavior. In: Proceedings of the First Workshop on Misinformation and Misbehavior Mining on the Web, pp. 1–6 (2016)
Popic, V., Yang, T., Vukovic, V., Desai, N., Ahamad, M.: File upload security: new attack vectors and countermeasures. In: Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, pp. 281–290 (2016)
Šajatović, M., Budiselić, E., Sušac, V.: A survey of honeypot deployment for detection of cyber attacks. In: 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), vol. 2020, pp. 1636–1641. IEEE (2020)
Feng, X., Zheng, Z., Cai, Z., Li, D., Li, J.: Defending against new malware with shared knowledge. In: 2014 IEEE International Conference on Communications (ICC), pp. 853–858. IEEE (2014)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)
Lipton, Z.C.: The mythos of model interpretability. Queue 16(3), 31–57 (2018)
Android Mischief Dataset. Stratosphere IPS. https://www.stratosphereips.org/android-mischief-dataset. Accessed 29 Oct 2023
Garg, S., Peddoju, S.K., Sarje, A.K.: Network-based detection of Android malicious apps. Int. J. Inf. Secur. 16, 385–400 (2017)
Sikder, A.K., Aksu, H., Uluagac, A.S.: 6thSense: a context-aware sensor-based attack detector for smart devices. In: Proceedings of the 26th USENIX Security Symposium, Vancouver, BC, Canada, pp. 397–414 (2017)
Salehi, M., Amini, M., Crispo, B.: Detecting malicious applications using system services request behavior. In: Proceedings of the 16th EAI International Conference on Mobile Ubiquitous System Computing, Networking Services, Houston, TX, USA, pp. 200–209 (2019)
Thangavelooa, R., **ga, W.W., Lenga, C.K., Abdullaha, J.: DATDroid: dynamic analysis technique in android malware detection. Int. J. Adv. Sci. Eng. Inf. Technol. 10, 536–541 (2020)
Lee, J., Park, S., Jung, J.: Detecting malicious behavior in Android apps through analyzing inter-app information flows. Expert Syst. Appl. 189, 116124 (2022)
Zhang, H., Chan, P.P., Cheung, N.M.: Android malware detection based on generative adversarial network. Neural Comput. Appl. (2023)
Acknowledgement
This work was conducted as part of the Artificial Intelligence for Development in Africa (AI4D Africa) program, with the financial support of Canada’s International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (Sida).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 IFIP International Federation for Information Processing
About this paper
Cite this paper
Nikiema, S.L., Sabane, A., Kabore, AK., Kafando, R., Bissyande, T.F. (2024). Detecting Illicit Data Leaks on Android Smartphones Using an Artificial Intelligence Models. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 712. Springer, Cham. https://doi.org/10.1007/978-3-031-63215-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-63215-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-63214-3
Online ISBN: 978-3-031-63215-0
eBook Packages: Computer ScienceComputer Science (R0)
