{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:03:43Z","timestamp":1760238223482,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Millions of Android applications (apps) are widely used today. Meanwhile, the number of malicious apps has increased exponentially. Currently, there are many security detection technologies for Android apps, such as static detection and dynamic detection. However, the uncertainty of the features in detection is not considered sufficiently in these technologies. Permissions play an important role in the security detection of Android apps. In this paper, a malicious application detection model based on features uncertainty (MADFU) is proposed. MADFU uses logistic regression function to describe the input (permissions) and output (labels) relationship. Moreover, it uses the Markov chain Monte Carlo (MCMC) algorithm to solve features\u2019 uncertainty. After experimenting with 2037 samples, for malware detection, MADFU achieves an accuracy of up to 95.5%, and the false positive rate (FPR) is 1.2%. MADFU\u2019s Android app detection accuracy is higher than the accuracy of directly using 24 dangerous permission. The results also indicate that the method for an unknown\/new sample\u2019s detection accuracy is 92.7%. Compared to other state-of-the-art approaches, the proposed method is more effective and efficient, by detecting malware.<\/jats:p>","DOI":"10.3390\/e22070792","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T10:59:38Z","timestamp":1595242778000},"page":"792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MADFU: An Improved Malicious Application Detection Method Based on Features Uncertainty"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9426-4191","authenticated-orcid":false,"given":"Hongli","family":"Yuan","sequence":"first","affiliation":[{"name":"Institute of information engineering, Anhui Xinhua University, Hefei 230088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2568-9628","authenticated-orcid":false,"given":"Yongchuan","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"67602","DOI":"10.1109\/ACCESS.2019.2918139","article-title":"Constructing features for detecting android malicious applications: Issues, taxonomy and directions","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"(2019, February 20). Data of AppBrain. Available online: https:\/\/www.appbrain.com\/stats\/number-of-android-apps."},{"key":"ref_3","unstructured":"(2019, February 18). 360 Internet Security Center. Available online: http:\/\/zt.360.cn\/1101061855.php?dtid=1101061451&did=610100815."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.infsof.2017.04.001","article-title":"Static analysis of android apps: A systematic literature review","volume":"88","author":"Li","year":"2017","journal-title":"Inf. Softw. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1109\/TIFS.2014.2353996","article-title":"Exploring permission-induced risk in Android applications for malicious application detection","volume":"9","author":"Wang","year":"2014","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.future.2018.11.021","article-title":"A machine learning based approach to detect malicious Android apps using discriminant system calls","volume":"94","author":"Vinod","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1109\/TIFS.2018.2806891","article-title":"Android malware familial classification and representative sample selection via frequent subgraph analysis","volume":"13","author":"Fan","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1109\/TIFS.2018.2879302","article-title":"DroidCat: Effective Android malware detection and categorization via app-level profiling","volume":"14","author":"Cai","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.engappai.2018.06.006","article-title":"CANDYMAN: Classifying Android malware families by modelling dynamic traces with Markov chains","volume":"74","author":"Camacho","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/TDSC.2016.2536605","article-title":"MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention","volume":"15","author":"Saracino","year":"2016","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3216","DOI":"10.1109\/TII.2017.2789219","article-title":"Significant permission identification for Machine-learning-based android malware detection","volume":"14","author":"Li","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1007\/s12652-018-0803-6","article-title":"Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network","volume":"10","author":"Wang","year":"2019","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"31798","DOI":"10.1109\/ACCESS.2018.2835654","article-title":"DroidEnsemble: Detecting Android malicious applications with ensemble of string and structural static features","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kumar, R., Xiaosong, Z., Khan, R.U., Kumar, J., and Ahad, I. (2018). Effective and explainable detection of Android malware based on machine learning algorithms. Proc. Int. Conf. Comput. Artif. Intell., 35\u201340.","DOI":"10.1145\/3194452.3194465"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pirscoveanu, R.S., Hansen, S.S., Larsen, T.M., Stevanovic, M., Pedersen, J.M., and Czech, A. (2015, January 8\u20139). Analysis of malware behavior: Type classification using machine learning. Proceedings of the 2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), London, UK.","DOI":"10.1109\/CyberSA.2015.7166115"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alzaylaee, M.K., Yerima, S.Y., and Sezer, S. (2017, January 24). Emulator vs. real phone: Android malware detection using machine learning. Proceedings of the 3rd ACM on International Workshop on Security and Privacy Analytics, New York, NY, USA.","DOI":"10.1145\/3041008.3041010"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1109\/TIFS.2016.2523912","article-title":"ICCDetector: ICC-based malware detection on Android","volume":"11","author":"Xu","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, K., Zhang, D., Su, X., and Li, W. (2015). Fest: A feature extraction and selection tool for Android malware detection. IEEE Symp. Comput. Commun., 714\u2013720.","DOI":"10.1109\/ISCC.2015.7405598"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, D., Wang, Z., Li, L., Wang, Z., Wang, Y., and Xue, Y. (2017, January 26\u201329). FgDetector: Fine-Grained Android Malware Detection. Proceedings of the 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), Shenzhen, China.","DOI":"10.1109\/DSC.2017.13"},{"key":"ref_20","first-page":"7303","article-title":"Entropy-based security risk measurement for Android mobile applications","volume":"23","author":"Mahmood","year":"2018","journal-title":"Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sarma, B.P., Li, N., Gates, C., Potharaju, R., Nita-Rotaru, C., and Molloy, I. (2012, January 3). Android permissions: A perspective combining risks and benefits. Proceedings of the 17th ACM Symposium on Access Control Models and Technologies, New York, NY, USA.","DOI":"10.1145\/2295136.2295141"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Peiravian, N., and Zhu, X. (2014, January 4\u20136). Machine learning for Android malware detection using permission and api calls. Proceedings of the 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, Washington, DC, USA.","DOI":"10.1109\/ICTAI.2013.53"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Su, D., Wang, W., Wang, X., and Liu, J. (2016, January 23\u201326). Anomadroid: Profiling Android applications\u2019 behaviors for identifying unknown malapps. Proceedings of the 2016 IEEE Trustcom\/BigDataSE\/ISPA, Tianjin, China.","DOI":"10.1109\/TrustCom.2016.0127"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gu, M., Sun, S., and Liu, Y. (2019). Dynamical sampling with langevin normalization flows. Entropy, 21.","DOI":"10.3390\/e21111096"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3074","DOI":"10.3390\/e16063074","article-title":"Information-geometric Markov chain Monte Carlo methods using diffusions","volume":"16","author":"Livingstone","year":"2014","journal-title":"Entropy"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hock, K., and Earle, K. (2016). Markov chain Monte Carlo used in parameter inference of magnetic resonance spectra. Entropy, 18.","DOI":"10.3390\/e18020057"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Q., and Newton, K. (2019). Diffusion equation-assisted Markov chain Monte Carlo methods for the inverse radiative transfer equation. Entropy, 21.","DOI":"10.3390\/e21030291"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Brooks, S., Gelman, A., Jones, G., and Meng, X. (2011). Handbook of Markov Chain Monte Carlo, CRC Press.","DOI":"10.1201\/b10905"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mneimneh, S., and Ahmed, S.A. (2016). Gibbs\/MCMC Sampling for Multiple RNA Interaction with Sub-optimal Solutions. Algorithms for Computational Biology, Springer.","DOI":"10.5220\/0005707900750084"},{"key":"ref_30","first-page":"1","article-title":"Efficient MCMC for Gibbs Random Fields using pre-computation","volume":"12","author":"Boland","year":"2017","journal-title":"Electron. J. Stats."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/MCS.2018.2876959","article-title":"Stationarity and convergence of the metropolis-hastings algorithm: Insights into theoretical aspects","volume":"39","author":"Hill","year":"2019","journal-title":"IEEE Control Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"329","DOI":"10.5322\/JES.2011.20.3.329","article-title":"Uncertainty analysis for parameters of probability distribution in rainfall frequency analysis by bayesian MCMC and metropolis hastings algorithm","volume":"20","author":"Seo","year":"2011","journal-title":"J. Environ. Sci. Int."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2002WR001642","article-title":"A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters","volume":"39","author":"Vrugt","year":"2003","journal-title":"Water Resour. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1029\/2005GB002468","article-title":"Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction","volume":"20","author":"Xu","year":"2006","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_35","unstructured":"(2020, February 02). The Drebin Dataset. Available online: https:\/\/www.sec.tu-bs.de\/~danarp\/drebin\/."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cui, Z., Zhang, M., and Chen, Y. (2018, January 17\u201318). Deep embedding logistic regression. Proceedings of the 2018 IEEE International Conference on Big Knowledge (ICBK), Singapore.","DOI":"10.1109\/ICBK.2018.00031"},{"key":"ref_37","unstructured":"(2020, February 10). Virscan. Available online: http:\/\/www.virscan.org\/."},{"key":"ref_38","unstructured":"(2020, February 10). Virustotal. Available online: https:\/\/www.virustotal.com\/."},{"key":"ref_39","unstructured":"(2020, February 12). Theano \u00b7 PyPI. Available online: https:\/\/pypi.org\/project\/Theano\/."},{"key":"ref_40","unstructured":"(2020, February 12). Pymc3 \u00b7 PyPI. Available online: https:\/\/pypi.org\/project\/pymc3\/."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/7\/792\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:06Z","timestamp":1760176206000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/7\/792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,20]]},"references-count":40,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["e22070792"],"URL":"https:\/\/doi.org\/10.3390\/e22070792","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2020,7,20]]}}}