Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI

Article


Senanayake, L., Kalutarage, H., Petrovski, A., Piras, L. and Al-Kadri, M. 2024. Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. Journal of Information Security and Applications. 82. https://doi.org/10.1016/j.jisa.2024.103741
TypeArticle
TitleDefendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI
AuthorsSenanayake, L., Kalutarage, H., Petrovski, A., Piras, L. and Al-Kadri, M.
Abstract

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to reduce potential vulnerabilities during development, their effectiveness in detecting vulnerabilities may fall short. To address this, “Defendroid”, a blockchain-based federated neural network enhanced with Explainable Artificial Intelligence (XAI) is introduced in this work. Trained on the LVDAndro dataset, the vanilla neural network model achieves a 96% accuracy and 0.96 F1-Score in binary classification for vulnerability detection. Additionally, in multi-class classification, the model accurately identifies Common Weakness Enumeration (CWE) categories with a 93% accuracy and 0.91 F1-Score. In a move to foster collaboration and model improvement, the model has been deployed within a blockchain-based federated environment. This environment enables community-driven collaborative training and enhancements in partnership with other clients. The extended model demonstrates improved accuracy of 96% and F1-Score of 0.96 in both binary and multi-class classifications. The use of XAI plays a pivotal role in presenting vulnerability detection results to developers, offering prediction probabilities for each word within the code. This model has been integrated into an Application Programming Interface (API) as the backend and further incorporated into Android Studio as a plugin, facilitating real-time vulnerability detection. Notably, Defendroid exhibits high efficiency, delivering prediction probabilities for a single code line in an average processing time of a mere 300 ms. The weight-sharing transparency in the blockchain-driven federated model enhances trust and traceability, fostering community engagement while preserving source code privacy and contributing to accuracy improvement.

KeywordsAndroid application protection; Code vulnerability; Neural network; Federated learning; Source code privacy; Explainable AI; Blockchain
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
Research GroupMDX Software Engineering, Theory and Algorithms (SETA) Research Group
PublisherElsevier
JournalJournal of Information Security and Applications
ISSN
Electronic2214-2126
Publication dates
Online05 Mar 2024
PrintMay 2024
Publication process dates
Accepted01 Mar 2024
Deposited23 May 2024
Output statusPublished
Publisher's version
License
File Access Level
Open
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jisa.2024.103741
LanguageEnglish
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