Abdullah, Z., Muhadi, F. W., Saudi, M. M., Hamid, I. R. A., & Foozy, C. F. M. (2020). Android ransomware detection based on dynamic obtained features. In Recent Advances on Soft Computing and Data Mining: Proceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, January 22–⁠ 23, 2020 (pp. 121-129). Springer International Publishing.
Ahmad, I., Basheri, M., Iqbal, M. J., & Rahim, A. (2018). Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE access, 6, 33789-33795.
Al Khayer, A., Almomani, I., & Elkawlak, K. (2020, November). ASAF: Android static analysis framework. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH) (pp. 197-202). IEEE.
Alenezi, M., & Almomani, I. (2017, October). Abusing android permissions: A security perspective. In 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (pp. 1-6). IEEE.
Alenezi, M., & Almomani, I. (2018). Empirical analysis of static code metrics for predicting risk scores in android appli-cations. In 5th International Symposium on Data Mining Applications (pp. 84-94). Springer International Publishing.
Al-Gethami, K. M., Al-Akhras, M. T., & Alawairdhi, M. (2021). Empirical evaluation of noise influence on supervised machine learning algorithms using intrusion detection datasets. Security and Communication Networks, 2021, 1-28.
Almomani, I., & Alenezi, M. (2019). Android application security scanning process. In Telecommunication Systems-Principles and Applications of Wireless-Optical Technologies. London, UK.: IntechOpen.
Almomani, I., & Khayer, A. (2019, April). Android applications scanning: The guide. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1-5). IEEE.
Alqatawna, J. F., Ala’M, A. Z., Hassonah, M. A., & Faris, H. (2021). Android botnet detection using machine learning models based on a comprehensive static analysis approach. Journal of Information Security and Applications, 58, 102735.
Alsoghyer, S., & Almomani, I. (2019). Ransomware detection system for Android applications. Electronics, 8(8), 868.
Alsoghyer, S., & Almomani, I. (2020, March). On the effectiveness of application permissions for Android ransomware detection. In 2020 6th conference on data science and machine learning applications (CDMA) (pp. 94-99). IEEE.
Amro, A., Al-Akhras, M., Hindi, K. E., Habib, M., & Shawar, B. A. (2021). Instance reduction for avoiding overfitting in decision trees. Journal of Intelligent Systems, 30(1), 438-459.
AndroidDeveloper. . 2020. https://developer.android.com/guide/topics/manifest/permission-element (ac-cessed 2 20, 2021).
AndroidDeveloper. Manifest.permission. 2020. https://developer.android.com/reference/android/Manifest.permission (ac-cessed 3 1, 2021).
AndroidDeveloper. Permissions on Android. 2020. https://developer.android.com/guide/topics/permissions/overview (ac-cessed 2 20, 2021).
AndroidDeveloper. SDK Platform release notes. 2020. https://developer.android.com/studio/releases/platforms (accessed 2 19, 2021).
AndroZoo. Home. 2020. https://androzoo.uni.lu/ (accessed 2 21, 2021).
Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K., & Siemens, C. E. R. T. (2014, February). Drebin: Effec-tive and explainable detection of android malware in your pocket. In Ndss (Vol. 14, pp. 23-26).
Arslan, R. S., Doğru, İ. A., & Barişçi, N. (2019). Permission-based malware detection system for android using machine learning techniques. International journal of software engineering and knowledge engineering, 29(01), 43-61.
Aswini, A. M., & Vinod, P. (2014, February). Droid permission miner: Mining prominent permissions for Android mal-ware analysis. In The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014) (pp. 81-86). IEEE.
Borah, P., Ahmed, H. A., & Bhattacharyya, D. K. (2014). A statistical feature selection technique. Network Modeling Analysis in Health Informatics and Bioinformatics, 3, 1-13.
Brownlee, J. (2018). Better deep learning: train faster, reduce overfitting, and make better predictions. Machine Learning Mastery.
Ceci,L. (accessed 02 24, 2021). StatistaResearchDepartment. Number of apps available in leading app stores 2020. 2 4, 2021. https://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores/
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling tech-nique. Journal of artificial intelligence research, 16, 321-357.
Choudhary, M., & Kishore, B. (2018, January). Haamd: Hybrid analysis for android malware detection. In 2018 Interna-tional Conference on Computer Communication and Informatics (ICCCI) (pp. 1-4). IEEE.
De La Iglesia, B. (2013). Evolutionary computation for feature selection in classification problems. Wiley Interdiscipli-nary Reviews: Data Mining and Knowledge Discovery, 3(6), 381-407.
Doğru, İ. A., & KİRAZ, Ö. (2018). Web-based android malicious software detection and classification system. Applied Sciences, 8(9), 1622.
Doğru, İ. A., & Önder, M. (2020). AppPerm analyzer: malware detection system based on android permissions and per-mission groups. International Journal of Software Engineering and Knowledge Engineering, 30(03), 427-450.
Dua, R., Ghotra, M. S., & Pentreath, N. (2017). Machine Learning with Spark. Packt Publishing Ltd.
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.".
Kanwal, N., & Bostanci, E. (2016). Comparative Study of Instance Based Learning and Back Propagation for Classifica-tion Problems. arXiv preprint arXiv:1604.05429.
Kerns, T. (accessed 2 19, 2021). There are now more than 2.5 billion active Android devices. 5 7, 2019. https://www.androidpolice.com/2019/05/07/there-are-now-more-than-2-5-billion-active-android-devices/
Khurma, R. A., Aljarah, I., & Sharieh, A. (2020, July). Rank based moth flame optimisation for feature selection in the medical application. In 2020 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE.
Kiss, N., Lalande, J. F., Leslous, M., & Tong, V. V. T. (2016). Kharon dataset: Android malware under a microscope. In The LASER Workshop: Learning from Authoritative Security Experiment Results (LASER 2016) (pp. 1-12).
Klein, K. (accessed 02 19, 2021). Industry Leaders Announce Open Platform for Mobile Devices. 11 05, 2007. http://www.openhandsetalliance.com/press_110507.html.
Krajci, I., Cummings, D., Krajci, I., & Cummings, D. (2013). History and Evolution of the Android OS. Android on x86: An Introduction to Optimizing for Intel® Architecture, 1-8.
Kumar, C. A., Sooraj, M. P., & Ramakrishnan, S. (2017). A comparative performance evaluation of supervised feature se-lection algorithms on microarray datasets. Procedia computer science, 115, 209-217.
Li, Y., Xiong, Z., Zhang, T., Zhang, Q., Fan, M., & Xue, L. (2022). Ensemble Framework Combining Family Information for Android Malware Detection. The Computer Journal, bxac114.
McAfee. McAfee Labs Threats Report - November 2020. McAfee, 2020.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
O’Dea, S. (2020). Global market share held by the leading smartphone operating systems in sales to end users from 1st quarter 2009 to 2nd quarter 2018.
Qamar, A., Karim, A., & Chang, V. (2019). Mobile malware attacks: Review, taxonomy & future directions. Future Gen-eration Computer Systems, 97, 887-909.
Russell, S. J. Norvig, P. (2020). Artificial intelligence: a modern approach. Pearson Education, Inc.
Susan, S., & Kumar, A. (2019). SSOMaj-SMOTE-SSOMin: Three-step intelligent pruning of majority and minority sam-ples for learning from imbalanced datasets. Applied Soft Computing, 78, 141-149.
Taylor, P. (accessed 2 24, 2021). https://www.statista.com/statistics/266136/global-market-share-held-by-smartphone-operating-systems/
Thon, J. "Static Analysis of Android Malware of 2017 ." Static Analysis of Android Malware of 2017 . Kaggle, 7 06, 2018.
Utku, A., & Dogru, İ. B. R. A. H. İ. M. (2017). Permission based detection system for android malware. Journal of the Faculty of Engineering and Architecture of Gazi University, 32(4).
Wang, Z. (2018). Deep learning-based intrusion detection with adversaries. IEEE Access, 6, 38367-38384.
Wei, F., Li, Y., Roy, S., Ou, X., & Zhou, W. (2017). Deep ground truth analysis of current android malware. In Detection of Intrusions and Malware, and Vulnerability Assessment: 14th International Conference, DIMVA 2017, Bonn, Germa-ny, July 6-7, 2017, Proceedings 14 (pp. 252-276). Springer International Publishing.
Wilson, D. R., & Martinez, T. R. (1997). Improved heterogeneous distance functions. Journal of artificial intelligence re-search, 6, 1-34.
Wilson, D. R., & Martinez, T. R. (2000). Reduction techniques for instance-based learning algorithms. Machine learning, 38, 257-286.
Witten, I. H., & Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java implementa-tions. Acm Sigmod Record, 31(1), 76-77.
Xiao, J., Chen, S., He, Q., Feng, Z., & Xue, X. (2020). An Android application risk evaluation framework based on mini-mum permission set identification. Journal of Systems and Software, 163, 110533.
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., ... & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. Ieee access, 6, 35365-35381.
Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International journal of bio-inspired computation, 2(2), 78-84.