How to cite this paper
AL-Akhras, M., Alghamdi, S., Omar, H & Alshareef, H. (2024). A machine learning technique for Android malicious attacks detection based on API calls.Decision Science Letters , 13(1), 29-44.
Refrences
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Abuthawabeh, M., & Mahmoud, K. W. (2020). Enhanced android malware detection and family classification, using conversation-level network traffic features. The International Arab Journal of Information Technology, 17(4A), 607-614.
Agrawal, P., & Trivedi, B. (2019, 20-22 Feb. 2019). A Survey on Android Malware and their Detection Techniques. Paper presented at the 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).
Alagarsamy, M., & Sathik, A. S. (2022). Context Aware Mobile Application Pre-Launching Model using KNN Classifier. International Arab Journal Of Information Technology, 19(6), 932-941.
Alam, S., & Demir, A. K. (2023). Mining android bytecodes through the eyes of gabor filters for detecting malware. The International Arab Journal of Information Technology, 20(2), 180-189.
Alenezi, M., & Almomani, I. (2018). Empirical analysis of static code metrics for predicting risk scores in android applications. In 5th International Symposium on Data Mining Applications (pp. 84-94). Springer International Publishing.
Almomani, I. M., & Khayer, A. A. (2020). A Comprehensive Analysis of the Android Permissions System. IEEE Access, 8, 216671-216688. doi: 10.1109/ACCESS.2020.3041432
Almomani, I., & Alenezi, M. (2019). Android application security scanning process. In Telecommunication Systems-Principles and Applications of Wireless-Optical Technologies. London, UK.: IntechOpen.
Alsoghyer, S., & Almomani, I. (2019). Ransomware Detection System for Android Applications. Electronics, 8(8). doi: 10.3390/electronics8080868
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. doi: doi:10.1515/jisys-2020-0061
AVTEST. (2021). Malware. from https://www.av-test.org/en/statistics/malware/
Buczak, A. L., & Guven, E. (2016). A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176. doi: 10.1109/COMST.2015.2494502
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
Dubey, H., Bhatt, S., & Negi, L.(2023) Digital Forensics Techniques and Trends: A Review. The International Arab Journal of Information Technology (IAJIT) 20(4), 644 - 654, doi: 10.34028/iajit/20/4/11.
Faris, H., Habib, M., Almomani, I., Eshtay, M., & Aljarah, I. (2020). Optimizing Extreme Learning Machines Using Chains of Salps for Efficient Android Ransomware Detection. Applied Sciences, 10(11). doi: 10.3390/app10113706
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning. arXiv preprint arXiv:1811.12560.
Hall, M. A. (1999). Correlation-based feature selection for machine learning (Doctoral dissertation, The University of Waikato).
Jung, J., Kim, H., Shin, D., Lee, M., Lee, H., Cho, S., & Suh, K. (2018, 26-28 Sept. 2018). Android Malware Detection Based on Useful API Calls and Machine Learning. Paper presented at the 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE).
Jung, J., Lim, K., Kim, B., Cho, S., Han, S., & Suh, K. (2019, 3-5 June 2019). Detecting Malicious Android Apps using the Popularity and Relations of APIs. Paper presented at the 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE).
Khayer, A. A., Almomani, I., & Elkawlak, K. (2020, 3-5 Nov. 2020). ASAF: Android Static Analysis Framework. Paper presented at the 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH).
Khurma, R. A., Aljarah, I., Sharieh, A., & Mirjalili, S. (2020). EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection. In S. Mirjalili, H. Faris & I. Aljarah (Eds.), Evolutionary Machine Learning Techniques: Algorithms and Applications (pp. 131-173). Singapore: Springer Singapore.
Lindorfer, M., Neugschwandtner, M., Weichselbaum, L., Fratantonio, Y., Van Der Veen, V., & Platzer, C. (2014, 2014). Andrubis--1,000,000 apps later: A view on current Android malware behaviors.
Louridas, P., & Ebert, C. (2016). Machine Learning. IEEE Software, 33(5), 110-115. doi: 10.1109/MS.2016.114
Nellaivadivelu, G., Di Troia, F., & Stamp, M. (2020). Black box analysis of android malware detectors. Array, 6, 100022. doi: https://doi.org/10.1016/j.array.2020.100022
Priyadarsini, R. P., Valarmathi, M. L., & Sivakumari, S. (2011). Gain ratio based feature selection method for privacy preservation. ICTACT Journal on soft computing, 1(4), 201-205.
Rathore, M. M., Ahmad, A., & Paul, A. (2016). Real time intrusion detection system for ultra-high-speed big data environments. The Journal of Supercomputing, 72(9), 3489-3510. doi: 10.1007/s11227-015-1615-5
Ray, S. (2019, 14-16 Feb. 2019). A Quick Review of Machine Learning Algorithms. Paper presented at the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon).
Sabar, N. R., Yi, X., & Song, A. (2018). A bi-objective hyper-heuristic support vector machines for big data cyber-security. IEEE Access, 6, 10421-10431.
Sarkar, A., Goyal, A., Hicks, D., Sarkar, D., & Hazra, S. (2019, 12-14 Dec. 2019). Android Application Development: A Brief Overview of Android Platforms and Evolution of Security Systems. Paper presented at the 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).
Seliya, N., Khoshgoftaar, T. M., & Hulse, J. V. (2009, 2-4 Nov. 2009). A Study on the Relationships of Classifier Performance Metrics. Paper presented at the 2009 21st IEEE International Conference on Tools with Artificial Intelligence.
Sharma, S., Challa, R. K., & Kumar, R. (2021). An ensemble-based supervised machine learning framework for android ransomware detection. The International Arab Journal of Information Technology, 18(3A), 422-429.
Sharma, T., & Rattan, D. (2021). Malicious application detection in android — A systematic literature review. Computer Science Review, 40, 100373. doi: https://doi.org/10.1016/j.cosrev.2021.100373
Van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373-440. doi: 10.1007/s10994-019-05855-6
Yang, F. (2019, 5-7 Dec. 2019). An Extended Idea about Decision Trees. Paper presented at the 2019 International Conference on Computational Science and Computational Intelligence (CSCI).
Zhang, Z., & Cai, H. (2019, May). A look into developer intentions for app compatibility in android. In 2019 IEEE/ACM 6th International Conference on Mobile Software Engineering and Systems (MOBILESoft) (pp. 40-44). IEEE.
Zhu, X., & Wu, X. (2004). Class Noise vs. Attribute Noise: A Quantitative Study. Artificial Intelligence Review, 22(3), 177-210. doi: 10.1007/s10462-004-0751-8
Abuthawabeh, M., & Mahmoud, K. W. (2020). Enhanced android malware detection and family classification, using conversation-level network traffic features. The International Arab Journal of Information Technology, 17(4A), 607-614.
Agrawal, P., & Trivedi, B. (2019, 20-22 Feb. 2019). A Survey on Android Malware and their Detection Techniques. Paper presented at the 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).
Alagarsamy, M., & Sathik, A. S. (2022). Context Aware Mobile Application Pre-Launching Model using KNN Classifier. International Arab Journal Of Information Technology, 19(6), 932-941.
Alam, S., & Demir, A. K. (2023). Mining android bytecodes through the eyes of gabor filters for detecting malware. The International Arab Journal of Information Technology, 20(2), 180-189.
Alenezi, M., & Almomani, I. (2018). Empirical analysis of static code metrics for predicting risk scores in android applications. In 5th International Symposium on Data Mining Applications (pp. 84-94). Springer International Publishing.
Almomani, I. M., & Khayer, A. A. (2020). A Comprehensive Analysis of the Android Permissions System. IEEE Access, 8, 216671-216688. doi: 10.1109/ACCESS.2020.3041432
Almomani, I., & Alenezi, M. (2019). Android application security scanning process. In Telecommunication Systems-Principles and Applications of Wireless-Optical Technologies. London, UK.: IntechOpen.
Alsoghyer, S., & Almomani, I. (2019). Ransomware Detection System for Android Applications. Electronics, 8(8). doi: 10.3390/electronics8080868
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. doi: doi:10.1515/jisys-2020-0061
AVTEST. (2021). Malware. from https://www.av-test.org/en/statistics/malware/
Buczak, A. L., & Guven, E. (2016). A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176. doi: 10.1109/COMST.2015.2494502
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
Dubey, H., Bhatt, S., & Negi, L.(2023) Digital Forensics Techniques and Trends: A Review. The International Arab Journal of Information Technology (IAJIT) 20(4), 644 - 654, doi: 10.34028/iajit/20/4/11.
Faris, H., Habib, M., Almomani, I., Eshtay, M., & Aljarah, I. (2020). Optimizing Extreme Learning Machines Using Chains of Salps for Efficient Android Ransomware Detection. Applied Sciences, 10(11). doi: 10.3390/app10113706
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning. arXiv preprint arXiv:1811.12560.
Hall, M. A. (1999). Correlation-based feature selection for machine learning (Doctoral dissertation, The University of Waikato).
Jung, J., Kim, H., Shin, D., Lee, M., Lee, H., Cho, S., & Suh, K. (2018, 26-28 Sept. 2018). Android Malware Detection Based on Useful API Calls and Machine Learning. Paper presented at the 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE).
Jung, J., Lim, K., Kim, B., Cho, S., Han, S., & Suh, K. (2019, 3-5 June 2019). Detecting Malicious Android Apps using the Popularity and Relations of APIs. Paper presented at the 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE).
Khayer, A. A., Almomani, I., & Elkawlak, K. (2020, 3-5 Nov. 2020). ASAF: Android Static Analysis Framework. Paper presented at the 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH).
Khurma, R. A., Aljarah, I., Sharieh, A., & Mirjalili, S. (2020). EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection. In S. Mirjalili, H. Faris & I. Aljarah (Eds.), Evolutionary Machine Learning Techniques: Algorithms and Applications (pp. 131-173). Singapore: Springer Singapore.
Lindorfer, M., Neugschwandtner, M., Weichselbaum, L., Fratantonio, Y., Van Der Veen, V., & Platzer, C. (2014, 2014). Andrubis--1,000,000 apps later: A view on current Android malware behaviors.
Louridas, P., & Ebert, C. (2016). Machine Learning. IEEE Software, 33(5), 110-115. doi: 10.1109/MS.2016.114
Nellaivadivelu, G., Di Troia, F., & Stamp, M. (2020). Black box analysis of android malware detectors. Array, 6, 100022. doi: https://doi.org/10.1016/j.array.2020.100022
Priyadarsini, R. P., Valarmathi, M. L., & Sivakumari, S. (2011). Gain ratio based feature selection method for privacy preservation. ICTACT Journal on soft computing, 1(4), 201-205.
Rathore, M. M., Ahmad, A., & Paul, A. (2016). Real time intrusion detection system for ultra-high-speed big data environments. The Journal of Supercomputing, 72(9), 3489-3510. doi: 10.1007/s11227-015-1615-5
Ray, S. (2019, 14-16 Feb. 2019). A Quick Review of Machine Learning Algorithms. Paper presented at the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon).
Sabar, N. R., Yi, X., & Song, A. (2018). A bi-objective hyper-heuristic support vector machines for big data cyber-security. IEEE Access, 6, 10421-10431.
Sarkar, A., Goyal, A., Hicks, D., Sarkar, D., & Hazra, S. (2019, 12-14 Dec. 2019). Android Application Development: A Brief Overview of Android Platforms and Evolution of Security Systems. Paper presented at the 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).
Seliya, N., Khoshgoftaar, T. M., & Hulse, J. V. (2009, 2-4 Nov. 2009). A Study on the Relationships of Classifier Performance Metrics. Paper presented at the 2009 21st IEEE International Conference on Tools with Artificial Intelligence.
Sharma, S., Challa, R. K., & Kumar, R. (2021). An ensemble-based supervised machine learning framework for android ransomware detection. The International Arab Journal of Information Technology, 18(3A), 422-429.
Sharma, T., & Rattan, D. (2021). Malicious application detection in android — A systematic literature review. Computer Science Review, 40, 100373. doi: https://doi.org/10.1016/j.cosrev.2021.100373
Van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373-440. doi: 10.1007/s10994-019-05855-6
Yang, F. (2019, 5-7 Dec. 2019). An Extended Idea about Decision Trees. Paper presented at the 2019 International Conference on Computational Science and Computational Intelligence (CSCI).
Zhang, Z., & Cai, H. (2019, May). A look into developer intentions for app compatibility in android. In 2019 IEEE/ACM 6th International Conference on Mobile Software Engineering and Systems (MOBILESoft) (pp. 40-44). IEEE.
Zhu, X., & Wu, X. (2004). Class Noise vs. Attribute Noise: A Quantitative Study. Artificial Intelligence Review, 22(3), 177-210. doi: 10.1007/s10462-004-0751-8