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Detecting Illicit Data Leaks on Android Smartphones Using an Artificial Intelligence Models

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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.

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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

  1. Centre d’Excellence en IA pour le Developpement (CITADEL), Ouagadougou, Burkina Faso

    Serge Lionel Nikiema, Rodrique Kafando & Tégawendé F. Bissyande

  2. Université Virtuelle du Burkina Faso, Ouagadougou, Burkina Faso

    Aminata Sabane, Rodrique Kafando & Tégawendé F. Bissyande

  3. Université Joseph Ki-Zerbo (UJKZ), Ouagadougou, Burkina Faso

    Abdoul-Kader Kabore, Rodrique Kafando & Tégawendé F. Bissyande

Authors
  1. Serge Lionel Nikiema

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  2. Aminata Sabane

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  3. Abdoul-Kader Kabore

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  4. Rodrique Kafando

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  5. Tégawendé F. Bissyande

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Corresponding author

Correspondence toSerge Lionel Nikiema.

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Abertay, Dundee, UK

    John Macintyre

  4. Informatics, Ionian University, Corfu, Greece

    Markos Avlonitis

  5. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

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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

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JPY 3498
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JPY 37751
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