Abstract
Nowadays, the expanding diffusion of Android phones along with the substantial usage of mobile applications is increasing the malware production. Among various malware threats, the rogue applications have expanded their growth in the field of smartphones, especially Android phones. This paper presents an optimal methodology to detect and classify rogue applications using image resemblance and opcode sequence reduction. First, the opcode sequences are extracted, and then, they are converted into gray images. After this, Linear Discriminant Analysis (LDA) is applied in two stages. LDA is a supervised probabilistic method that is used for class separation and size reduction. In the first stage, the image sizes are reduced by selecting only the optimal features using LDA. The main objective of this stage is to increase the accuracy rate by reducing the size of opcode sequences. In the next stage, LDA is applied to test and train the dataset samples for separating rogue and benign apps. The experimental results on the rogue application families and unknown rogue apps show that the proposed methodology is efficiently able to identify rogue apps with an accuracy rate of 96.5%.
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References
Faruki, P., Bharmal, A., Laxmi, V., Ganmoor, V., Gaur, M.S., Conti, M., Rajarajan, M.: Android security: a survey of issues, malware penetration, and defenses. IEEE Commun. Surv. Tutor. 17(2), 998–1022 (2014)
Jerome, Q., Allix, K., State, R., Engel, T.: Using opcode-sequences to detect malicious android applications. In: 2014 IEEE International Conference on Communications (ICC), pp. 914–919. IEEE (2014)
Li, J., Sun, L., Yan, Q., Li, Z., Srisa-an, W., Ye, H.: Significant permission identification for machine-learning-based android malware detection. IEEE Trans. Ind. Inform. 14(7), 3216–3225 (2018)
Ma, Z., Ge, H., Liu, Y., Zhao, M., Ma, J.: A combination method for android malware detection based on control flow graphs and machine learning algorithms. IEEE Access 7, 21235–21245 (2019)
Nataraj, L., Karthikeyan, S., Jacob, G., Manjunath, B.: Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security, p. 4. ACM (2011)
Sahs, J., Khan, L.: A machine learning approach to android malware detection. In: 2012 European Intelligence and Security Informatics Conference, pp. 141–147. IEEE (2012)
Wei, L., Luo, W., Weng, J., Zhong, Y., Zhang, X., Yan, Z.: Machine learning-based malicious application detection of android. IEEE Access 5, 25591–25601 (2017)
Xiaoyan, Z., Juan, F., Xiujuan, W.: Android malware detection based on permissions (2014)
Yang, X., Lo, D., Li, L., Xia, X., Bissyandé, T.F., Klein, J.: Characterizing malicious android apps by mining topic-specific data flow signatures. Inf. Softw. Technol. 90, 27–39 (2017)
Yerima, S.Y., Sezer, S., McWilliams, G., Muttik, I.: A new android malware detection approach using bayesian classification. In: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), pp. 121–128. IEEE (2013)
Zachariah, R., Akash, K., Yousef, M.S., Chacko, A.M.: Android malware detection a survey. In: 2017 IEEE International Conference on Circuits and Systems (ICCS), pp. 238–244. IEEE (2017)
Zhang, J., Qin, Z., Yin, H., Ou, L., Xiao, S., Hu, Y.: Malware variant detection using opcode image recognition with small training sets. In: 2016 25th International Conference on Computer Communication and Networks (ICCCN), pp. 1–9. IEEE (2016)
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Acharya, S., Rawat, U., Bhatnagar, R. (2021). Android Rogue Application Detection Using Image Resemblance and Reduced LDA. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore. https://doi.org/10.1007/978-981-15-3383-9_25
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DOI: https://doi.org/10.1007/978-981-15-3383-9_25
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