Abstract
Android, the most popular mobile operating system, with billions of active users and more than 2 million apps, has motivated advertisers, hackers, fraudsters and cyber-criminals to develop malware of all types for it. In recent years, extensive research has been conducted on malware analysis and detection for Android devices, even though Android has already implemented various security mechanisms to deal with the problem. In this paper, we developed a consortium blockchain network to evaluate various machine learning models for a given malware dataset. A reward is offered using smart contracts as an incentive to the competitors for their work by allowing them to submit solutions through training with selected machine learning models in a secure and trustworthy manner. The analysis of datasets by competitors helps various organizations in the network to enhance or boost their current malware detection or defense tools. The decentralized network provides transparency, enhances security and reduces the cost in managing all relevant data by eliminating third parties. We used DREBIN dataset in the developed framework for initial experiments and the encouraging results are presented.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Drake, J.J., Lanier, Z., Mulliner, C., Fora, P.O., Ridley, S.A., Wicherski, G.: Android Hacker’s Handbook. Wiley, Indianapolis (2014)
Rana, M.S., Sung, A.H.: Malware analysis on android using supervised machine learning techniques. Int. J. Comput. Commun. Eng. 7(4), 178–188 (2018)
Rana, M.S., Rahman, S.S.M.M., Sung, A.H.: Evaluation of tree based machine learning classifiers for android malware detection. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds.) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science, vol. 11056. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98446-9_35
Rana, M.S., Gudla, C., Sung, A.H.: Android malware detection using stacked generalization. In: Proceeding of 27th International Conference on Software Engineering and Data Engineering, pp. 15–19 (2018)
Rana, M.S., Gudla, C., Sung, A.H.: Evaluating machine learning models for android malware detection – a comparison study. In: Proceeding of International Conference on Network, Communication, and Computing, Taipei, Taiwan (2018)
Enck, W., Gilbert, P., Chun, B., Cox, L.P., Jung, J., McDaniel, P., Sheth, A.: Taintdroid: an information-flow tracking system for real-time privacy monitoring on smartphones. In: Proceeding of USENIX Symposium on Operating Systems Design and Implementation (OSDI), pp. 393–407 (2010)
Zhou, Y., Wang, Z., Zhou, W., Jiang, X.: Hey, you, get off my market: detecting malicious apps in official and alternative android markets. In: Proceeding of Network and Distributed System Security Symposium (NDSS) (2012)
Yan, L.K., Yin, H.: Droidscope: seamlessly reconstructing OS and dalvik semantic views for dynamic android malware analysis. In: Proceeding of USENIX Security Symposium (2012)
Enck, W., Ongtang, M., McDaniel, P.D.: On lightweight mobile phone application certification. In: Proceeding of ACM Conference on Computer and Communications Security (CCS), pp. 235–245 (2009)
Felt, A.P., Chin, E., Hanna, S., Song, D., and Wagner, D.: Android permissions demystified. In: Proceeding of ACM Conference on Computer and Communications Security (CCS), pp. 627–638 (2011)
Grace, M., Zhou, Y., Zhang, Q., Zou, S., Jiang, X.: Risk-ranker: scalable and accurate zero-day android malware detection. In: Proceeding of International Conference on Mobile Systems, Applications, and Services (MOBISYS), pp. 281–294 (2012)
Kurtulmus, A.B., Daniel, K.: Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain, Algorithmia Research (2018). https://algorithmia.com/static/documents/d3a4c04/Machine-Learning-Models-on-the-Ethereum-Blockchain.pdf. Accessed 18 Sept 2018
Gu, J., Sun, B., Du, X., Wang, J., Zhuang, Y., Wang, Z.: Consortium blockchain-based malware detection in mobile devices. In: IEEE Access, vol. 6, pp. 12118–12128 (2018). https://doi.org/10.1109/access.2018.2805783
Raje, S., Vaderia, S., Wilson, N., Panigrahi, R.: Decentralised firewall for malware detection. In: 2017 International Conference on Advances in Computing, Communication and Control (ICAC3), pp. 1–5 (2017)
Ouaguid, A., Abghour, N., Ouzzif, M.: A novel security framework for managing android permissions using blockchain technology. Int. J. Cloud Appl. Comput. (IJCAC) 8(1), 55–79 (2018)
Noyes, C.: BitAV: Fast Anti-Malware by Distributed Blockchain Consensus and Feedforward Scanning, CoRR, abs/1601.01405 (2016)
Firdaus, A., Anuar, N.B., Razak, M.F., Hashem, I.A., Bachok, S., Sangaiah, A.K.: Root exploit detection and features optimization: mobile device and blockchain based medical data management. J. Med. Syst. 42, 1–23 (2018)
Moubarak, J., Filiol, E., Chamoun, M.: Developing a K-ary malware using Blockchain. https://arxiv.org/abs/1804.01488. Accessed 20 Oct 2018
Decision Tree – Classification. https://www.saedsayad.com/decision_tree.htm. Accessed 20 Oct 2018
Towards Data Science | The Random Forest Algorithm. https://towards-datascience.com/the-random-forest-algorithm-d457d499ffcd. Accessed 20 Oct 2018
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
A Comprehensive Guide to Ensemble Learning. https://www.analyticsvidhya.com/-blog/2018/06/comprehensive-guide-for-ensemble-models/. Accessed 20 Oct 2018
Towards Data Science | Support Vector Machine - Introduction to Machine Learning Algorithms. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47. Accessed 20 Oct 2018
Neural Networks with Scikit. https://www.python-course.eu/neural-networks-with-scikit.php. Accessed 20 Oct 2018
Naive Bayes for Machine Learning. https://machinelearningmastery.com/naive-bayes-for-machine-learning/. Accessed 20 Oct 2018
K-Nearest Neighbors for Machine Learning. https://machinelearningmastery.com/k-nearest-neighbors-for-machine-learning/. Accessed 20 Oct 2018
Discriminant Analysis. https://ncss-wpengine.netdna-ssl.com/wp-content/themes/nc-ss/pdf/Procedures/NCSS/Discriminant_Analysis.pdf. Accessed 20 Oct 2018
Towards Data Science | Logistic Regression - Detailed Overview. https://towards-datascience.com/logistic-regression-detailed-overview-46c4da4303bc. Accessed 20 Oct 2018
Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K.: DREBIN: effective and explainable detection of android malware in your pocket. In: NDSS, vol. 14, pp. 23–26, USA (2014)
Confusion Matrix. http://www2.cs.uregina.ca/~dbd/cs831/notes/confusion-matrix/confusion-matrix.html. Accessed 20 Oct 2018
Simple guide to confusion matrix terminology. http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/. Accessed 20 Oct 2018
Acknowledgment
The authors wish to acknowledge the valuable help received from Besir Kurtulmus, Algorithmia Inc., for his guidance on technology and domain knowledge pertaining to applying machine learning within blockchain.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rana, M.S., Gudla, C., Sung, A.H. (2019). Evaluating Machine Learning Models on the Ethereum Blockchain for Android Malware Detection. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 998. Springer, Cham. https://doi.org/10.1007/978-3-030-22868-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-22868-2_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22867-5
Online ISBN: 978-3-030-22868-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)