Abstract
The popularity of the Android operating system has been rising ever since its initial release in 2008. This is due to two major reasons. The first is that Android is open-source, due to which a lot of mobile manufacturing companies use some form of modified Android OS for their devices. The second reason is that a wide variety of applications with different designs and utility can be built with ease for Android devices. With this much popularity, gaining unwanted attention of cybercriminals is inevitable. Hence, there has been a huge rise in the number of malware being developed for Android devices. To address this problem, we present ADAM (Automatic Detection of Android Malware), an Android application that uses machine learning (ML) for automatic detection of malware in Android applications. ADAM is trained with CICMalDroid 2020 Android Malware dataset and tested for both CICMalDroid 2020 and CICMalDroid 2017 dataset. The experiment analysis showed that it achieves more than 98.5% accuracy. ADAM considers only static analysis, so becomes easy to deploy in smart phone to alert the user. ADAM is deployed over android mobile phone.
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References
The international data corporation IDC: smartphone OS market share, 2016 Q2 (2016). http://www.idc.com/prodserv/smartphone-os-market-share.jsp
Kaspersky: The number of new malicious files detected every day (2020). https://www.kaspersky.com/about/press-releases/2020_the-number-of-new-malicious-files-detected-every-day-increases-by-52-to-360000-in-2020
Number of android apps on google play. https://www.appbrain.com/stats/number-of-android-apps. Accessed 30 Apr 2020
Afonso, V.M., de Amorim, M.F., Grégio, A.R.A., Junquera, G.B., de Geus, P.L.: Identifying android malware using dynamically obtained features. J. Comput. Virol. Hacking Tech. 11(1), 9–17 (2015)
Alzaylaee, M.K., Yerima, S.Y., Sezer, S.: DL-droid: deep learning based android malware detection using real devices. Comput. Secur. 89, 101663 (2020)
Amankwah, R., Kudjo, P.K., Antwi, S.Y.: Evaluation of software vulnerability detection methods and tools: a review. Int. J. Comput. Appl. 169(8), 22–7 (2017)
Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K., Siemens, C.: DREBIN: effective and explainable detection of android malware in your pocket. In: Ndss, vol. 14, pp. 23–26 (2014)
Burguera, I., Zurutuza, U., Nadjm-Tehrani, S.: Crowdroid: behavior-based malware detection system for android. In: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, pp. 15–26 (2011)
Cai, H., Meng, N., Ryder, B., Yao, D.: DroidCat: effective android malware detection and categorization via app-level profiling. IEEE Trans. Inf. Forensics Secur. 14(6), 1455–1470 (2018)
Enck, W., et al.: TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones. ACM Trans. Comput. Syst. (TOCS) 32(2), 1–29 (2014)
Gao, T., Peng, W., Sisodia, D., Saha, T.K., Li, F., Al Hasan, M.: Android malware detection via graphlet sampling. IEEE Trans. Mob. Comput. 18(12), 2754–2767 (2018)
Glodek, W., Harang, R.: Rapid permissions-based detection and analysis of mobile malware using random decision forests. In: MILCOM 2013–2013 IEEE Military Communications Conference, pp. 980–985. IEEE (2013)
Hui, H., Zhi, Y., Xi, N., Liu, Y.: A weighted voting framework for android app’s vetting based on multiple machine learning models. In: Kutyłowski, M., Zhang, J., Chen, C. (eds.) NSS 2020. LNCS, vol. 12570, pp. 63–78. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65745-1_4
Lashkari, A.H., Kadir, A.F.A., Taheri, L., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark android malware datasets and classification. In: 2018 International Carnahan Conference on Security Technology (ICCST), pp. 1–7. IEEE (2018)
Li, D., Wang, Z., Xue, Y.: DeepDetector: android malware detection using deep neural network. In: 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), pp. 184–188. IEEE (2018)
Liu, K., Xu, S., Xu, G., Zhang, M., Sun, D., Liu, H.: A review of android malware detection approaches based on machine learning. IEEE Access 8, 124579–124607 (2020)
McGiff, J., Hatcher, W.G., Nguyen, J., Yu, W., Blasch, E., Lu, C.: Towards multimodal learning for android malware detection. In: 2019 International Conference on Computing, Networking and Communications (ICNC), pp. 432–436. IEEE (2019)
Peiravian, N., Zhu, X.: Machine learning for android malware detection using permission and API calls. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, pp. 300–305. IEEE (2013)
Rahali, A., Lashkari, A.H., Kaur, G., Taheri, L., Gagnon, F., Massicotte, F.: DIDroid: android malware classification and characterization using deep image learning. In: 2020 the 10th International Conference on Communication and Network Security, pp. 70–82 (2020)
Santos, I., Brezo, F., Ugarte-Pedrero, X., Bringas, P.G.: Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf. Sci. 231, 64–82 (2013)
Tam, K., Fattori, A., Khan, S., Cavallaro, L.: Copperdroid: automatic reconstruction of android malware behaviors. In: NDSS Symposium 2015, pp. 1–15 (2015)
Tan, D.J., Chua, T.W., Thing, V.L.: Securing android: a survey, taxonomy, and challenges. ACM Comput. Surv. (CSUR) 47(4), 1–45 (2015)
Wu, C., Shi, J., Yang, Y., Li, W.: Enhancing machine learning based malware detection model by reinforcement learning. In: Proceedings of the 8th International Conference on Communication and Network Security, pp. 74–78 (2018)
Xiao, X., Xiao, X., Jiang, Y., Liu, X., Ye, R.: Identifying android malware with system call co-occurrence matrices. Trans. Emerg. Telecommun. Technol. 27(5), 675–684 (2016)
Zhao, L., Li, D., Zheng, G., Shi, W.: Deep neural network based on android mobile malware detection system using opcode sequences. In: 2018 IEEE 18th International Conference on Communication Technology (ICCT), pp. 1141–1147. IEEE (2018)
Zhao, M., Ge, F., Zhang, T., Yuan, Z.: AntiMalDroid: an efficient SVM-based malware detection framework for android. In: Liu, C., Chang, J., Yang, A. (eds.) ICICA 2011. CCIS, vol. 243, pp. 158–166. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-27503-6_22
Zhu, H.J., You, Z.H., Zhu, Z.X., Shi, W.L., Chen, X., Cheng, L.: DroidDet: effective and robust detection of android malware using static analysis along with rotation forest model. Neurocomputing 272, 638–646 (2018)
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Tripathy, S., Singh, N., Singh, D.N. (2022). ADAM: Automatic Detection of Android Malware. In: Ryan, P.Y., Toma, C. (eds) Innovative Security Solutions for Information Technology and Communications. SecITC 2021. Lecture Notes in Computer Science, vol 13195. Springer, Cham. https://doi.org/10.1007/978-3-031-17510-7_2
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DOI: https://doi.org/10.1007/978-3-031-17510-7_2
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