How to cite this paper
Samara, G., Al-Mohtaseb, A., Khafajeh, H., Alazaidah, R., Alidmat, O., Nasayreh, A., Alzyoud, M & Al-shanableh, N. (2024). Securing cryptocurrency transactions: Innovations in malware detection using machine learning.International Journal of Data and Network Science, 8(4), 2055-2066.
Refrences
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Adeniyi, D. A., Wei, Z., & Yongquan, Y. (2016). Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics, 12(1), 90-108.
Alam, M. S., Husain, D., Naqvi, S., & Kumar, P. (2018). IOT security through Machine Learning and homographic en-cryption technique. In International conference on new trends in engineering & technology (ICNTET), Chennai.
Alazaidah, R., Al-Shaikh, A., Al-Mousa, M. R., Khafajah, H., Samara, G., Alzyoud, M., & Almatarneh, S. (2024). Web-site phishing detection using machine learning techniques. Journal of Statistics Applications & Probability, 13(1), 119-129.
Alazaidah, R., Samara, G., Aljaidi, M., Haj Qasem, M., Alsarhan, A., & Alshammari, M. (2023a). Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Mod-els. Diagnostics, 14(1), 27.
Alazaidah, R., Samara, G., Almatarneh, S., Hassan, M., Aljaidi, M., & Mansur, H. (2023b). Multi-label classification based on associations. Applied Sciences, 13(8), 5081.
Al-Daoud, E. (2007). Quantum Computing for Solving a System of Nonlinear Equations over GF (q). Int. Arab J. Inf. Technol., 4(3), 201-205.
Al-Fayoumi, M., Al-Haija, Q. A., Armoush, R., & Amareen, C. (2024). XAI-PDF: a robust framework for malicious PDF detection leveraging SHAP-based feature engineering. Int. Arab J. Inf. Technol., 21(1), 128-146.
Al-Haija, Q. A., & Alsulami, A. A. (2021). High performance classification model to identify ransomware payments for heterogeneous bitcoin networks. Electronics, 10(17), 2113.
Alhawi, O. M., Baldwin, J., & Dehghantanha, A. (2018). Leveraging machine learning techniques for windows ransom-ware network traffic detection. Cyber threat intelligence, 93-106.
Aljaidi, M., Alsarhan, A., Samara, G., Alazaidah, R., Almatarneh, S., Khalid, M., & Al-Gumaei, Y. A. (2022, November). NHS WannaCry ransomware attack: technical explanation of the vulnerability, exploitation, and countermeasures. In 2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) (pp. 1-6). IEEE.
Al-rimy, B. A. S., Maarof, M. A., & Shaid, S. Z. M. (2019). Crypto-ransomware early detection model using novel in-cremental bagging with enhanced semi-random subspace selection. Future Generation Computer Systems, 101, 476-491.
Al-Shanableh, N., Al-Zyoud, M., Al-Husban, R. Y., Al-Shdayfat, N., Alkhawaldeh, J. F. M., Alajarmeh, N. S., & Al-Hawary, S. I. S. (2024). Data Mining to Reveal Factors Associated with Quality of life among Jordanian Women with Breast Cancer. Applied Mathematics, 18(2), 403-408.
Alzyoud, M., Al-Shanableh, N., Alomar, S., AsadAlnaser, A., Mustafad, A., Al-Momani, A., & Al-Hawary, S. (2024). Artificial intelligence in Jordanian education: Assessing acceptance via perceived cybersecurity, novelty value, and perceived trust. International Journal of Data and Network Science, 8(2), 823-834.
Bahrami, P. N., Dehghantanha, A., Dargahi, T., Parizi, R. M., Choo, K. K. R., & Javadi, H. H. (2019). Cyber kill chain-based taxonomy of advanced persistent threat actors: Analogy of tactics, techniques, and procedures. Journal of in-formation processing systems, 15(4), 865-889.
Baldwin, J., & Dehghantanha, A. (2018). Leveraging support vector machine for opcode density based detection of cryp-to-ransomware. Cyber threat intelligence, 107-136.
Bowers, A. J., & Zhou, X. (2019). Receiver operating characteristic (ROC) area under the curve (AUC): A diagnostic measure for evaluating the accuracy of predictors of education outcomes. Journal of Education for Students Placed at Risk (JESPAR), 24(1), 20-46.
Bzdok, D., & Meyer-Lindenberg, A. (2018). Machine learning for precision psychiatry: opportunities and challeng-es. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(3), 223-230.
Chen, L., Yang, C. Y., Paul, A., & Sahita, R. (2018). Towards resilient machine learning for ransomware detection. arXiv preprint arXiv:1812.09400.
Darabian, H., Homayounoot, S., Dehghantanha, A., Hashemi, S., Karimipour, H., Parizi, R. M., & Choo, K. K. R. (2020). Detecting cryptomining malware: a deep learning approach for static and dynamic analysis. Journal of Grid Compu-ting, 18, 293-303.
Dey, S. (2018, September). Securing majority-attack in blockchain using machine learning and algorithmic game theory: A proof of work. In 2018 10th computer science and electronic engineering (CEEC) (pp. 7-10). IEEE.
Hand, D. J., Christen, P., & Kirielle, N. (2021). F*: an interpretable transformation of the F-measure. Machine Learn-ing, 110(3), 451-456.
Homayoun, S., Dehghantanha, A., Ahmadzadeh, M., Hashemi, S., & Khayami, R. (2017). Know abnormal, find evil: fre-quent pattern mining for ransomware threat hunting and intelligence. IEEE transactions on emerging topics in com-puting, 8(2), 341-351.
Hussain, I., Samara, G., Ullah, I., & Khan, N. (2021, December). Encryption for end-user privacy: A cyber-secure smart energy management system. In 2021 22nd International Arab Conference on Information Technology (ACIT) (pp. 1-6). IEEE.
Kulkarni, A., Chong, D., & Batarseh, F. A. (2020). Foundations of data imbalance and solutions for a data democracy. In Data democracy (pp. 83-106). Academic Press.
Li, L., Ma, S., & Zhang, Y. (2014, December). Optimization algorithm based on genetic support vector machine model. In 2014 Seventh International Symposium on Computational Intelligence and Design (Vol. 1, pp. 307-310). IEEE.
Liu, W., Liang, X., & Cui, G. (2020). Common risk factors in the returns on cryptocurrencies. Economic Modelling, 86, 299-305.
Maxmen, A. (2018). AI researchers embrace Bitcoin technology to share medical data. Nature, 555(7697), 293-295.
Musa, N. S., Mirza, N. M., & Ali, A. (2023). Navigating the Complex Landscape of IoT Forensics: Challenges and Emerging Solutions.
Paquet-Clouston, M., Haslhofer, B., & Dupont, B. (2019). Ransomware payments in the bitcoin ecosystem. Journal of Cy-bersecurity, 5(1), tyz003.
Samara, G. (2020, November). Wireless sensor network MAC energy-efficiency protocols: a survey. In 2020 21st Inter-national Arab Conference on Information Technology (ACIT) (pp. 1-5). IEEE.
Samara, G., & Al-okour, M. (2020). Optimal number of cluster heads in wireless sensors networks based on LEACH. arXiv preprint arXiv:2003.13765.
Samara, G., Al-Daoud, E., Swerki, N., & Alzu’bi, D. (2023). The recognition of holy qur’an reciters using the mfccs’ technique and deep learning. Advances in Multimedia, 2023(1), 2642558.
Samara, G., Besani, G. A., Alauthman, M., & Khaldy, M. A. (2020). Energy-efficiency routing algorithms in wireless sensor networks: A survey. arXiv preprint arXiv:2002.07178.
Samara, G., Elhilo, A., Asassfeh, M. R., Injadat, M., Qasem, M. H., Alazaidah, R., & Hnaif, A. A. (2023, December). Op-timizing Road Safety through Intelligent Congestion Management. In 2023 24th International Arab Conference on Information Technology (ACIT) (pp. 1-7). IEEE.
Takeuchi, Y., Sakai, K., & Fukumoto, S. (2018, August). Detecting ransomware using support vector machines. In Workshop Proceedings of the 47th International Conference on Parallel Processing (pp. 1-6).
Vasan, D., Alazab, M., Wassan, S., Naeem, H., Safaei, B., & Zheng, Q. (2020). IMCFN: Image-based malware classifica-tion using fine-tuned convolutional neural network architecture. Computer Networks, 171, 107138.
Yazdinejad, A., HaddadPajouh, H., Dehghantanha, A., Parizi, R. M., Srivastava, G., & Chen, M. Y. (2020). Cryptocurren-cy malware hunting: A deep recurrent neural network approach. Applied Soft Computing, 96, 106630.
Yin, M., Wortman Vaughan, J., & Wallach, H. (2019, May). Understanding the effect of accuracy on trust in machine learning models. In Proceedings of the 2019 chi conference on human factors in computing systems (pp. 1-12).
Yuan, B., Wang, J., Liu, D., Guo, W., Wu, P., & Bao, X. (2020). Byte-level malware classification based on markov im-ages and deep learning. Computers & Security, 92, 101740.
Yuan, Z., Lu, Y., & Xue, Y. (2016). Droiddetector: android malware characterization and detection using deep learn-ing. Tsinghua Science and Technology, 21(1), 114-123.
Adeniyi, D. A., Wei, Z., & Yongquan, Y. (2016). Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics, 12(1), 90-108.
Alam, M. S., Husain, D., Naqvi, S., & Kumar, P. (2018). IOT security through Machine Learning and homographic en-cryption technique. In International conference on new trends in engineering & technology (ICNTET), Chennai.
Alazaidah, R., Al-Shaikh, A., Al-Mousa, M. R., Khafajah, H., Samara, G., Alzyoud, M., & Almatarneh, S. (2024). Web-site phishing detection using machine learning techniques. Journal of Statistics Applications & Probability, 13(1), 119-129.
Alazaidah, R., Samara, G., Aljaidi, M., Haj Qasem, M., Alsarhan, A., & Alshammari, M. (2023a). Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Mod-els. Diagnostics, 14(1), 27.
Alazaidah, R., Samara, G., Almatarneh, S., Hassan, M., Aljaidi, M., & Mansur, H. (2023b). Multi-label classification based on associations. Applied Sciences, 13(8), 5081.
Al-Daoud, E. (2007). Quantum Computing for Solving a System of Nonlinear Equations over GF (q). Int. Arab J. Inf. Technol., 4(3), 201-205.
Al-Fayoumi, M., Al-Haija, Q. A., Armoush, R., & Amareen, C. (2024). XAI-PDF: a robust framework for malicious PDF detection leveraging SHAP-based feature engineering. Int. Arab J. Inf. Technol., 21(1), 128-146.
Al-Haija, Q. A., & Alsulami, A. A. (2021). High performance classification model to identify ransomware payments for heterogeneous bitcoin networks. Electronics, 10(17), 2113.
Alhawi, O. M., Baldwin, J., & Dehghantanha, A. (2018). Leveraging machine learning techniques for windows ransom-ware network traffic detection. Cyber threat intelligence, 93-106.
Aljaidi, M., Alsarhan, A., Samara, G., Alazaidah, R., Almatarneh, S., Khalid, M., & Al-Gumaei, Y. A. (2022, November). NHS WannaCry ransomware attack: technical explanation of the vulnerability, exploitation, and countermeasures. In 2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) (pp. 1-6). IEEE.
Al-rimy, B. A. S., Maarof, M. A., & Shaid, S. Z. M. (2019). Crypto-ransomware early detection model using novel in-cremental bagging with enhanced semi-random subspace selection. Future Generation Computer Systems, 101, 476-491.
Al-Shanableh, N., Al-Zyoud, M., Al-Husban, R. Y., Al-Shdayfat, N., Alkhawaldeh, J. F. M., Alajarmeh, N. S., & Al-Hawary, S. I. S. (2024). Data Mining to Reveal Factors Associated with Quality of life among Jordanian Women with Breast Cancer. Applied Mathematics, 18(2), 403-408.
Alzyoud, M., Al-Shanableh, N., Alomar, S., AsadAlnaser, A., Mustafad, A., Al-Momani, A., & Al-Hawary, S. (2024). Artificial intelligence in Jordanian education: Assessing acceptance via perceived cybersecurity, novelty value, and perceived trust. International Journal of Data and Network Science, 8(2), 823-834.
Bahrami, P. N., Dehghantanha, A., Dargahi, T., Parizi, R. M., Choo, K. K. R., & Javadi, H. H. (2019). Cyber kill chain-based taxonomy of advanced persistent threat actors: Analogy of tactics, techniques, and procedures. Journal of in-formation processing systems, 15(4), 865-889.
Baldwin, J., & Dehghantanha, A. (2018). Leveraging support vector machine for opcode density based detection of cryp-to-ransomware. Cyber threat intelligence, 107-136.
Bowers, A. J., & Zhou, X. (2019). Receiver operating characteristic (ROC) area under the curve (AUC): A diagnostic measure for evaluating the accuracy of predictors of education outcomes. Journal of Education for Students Placed at Risk (JESPAR), 24(1), 20-46.
Bzdok, D., & Meyer-Lindenberg, A. (2018). Machine learning for precision psychiatry: opportunities and challeng-es. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(3), 223-230.
Chen, L., Yang, C. Y., Paul, A., & Sahita, R. (2018). Towards resilient machine learning for ransomware detection. arXiv preprint arXiv:1812.09400.
Darabian, H., Homayounoot, S., Dehghantanha, A., Hashemi, S., Karimipour, H., Parizi, R. M., & Choo, K. K. R. (2020). Detecting cryptomining malware: a deep learning approach for static and dynamic analysis. Journal of Grid Compu-ting, 18, 293-303.
Dey, S. (2018, September). Securing majority-attack in blockchain using machine learning and algorithmic game theory: A proof of work. In 2018 10th computer science and electronic engineering (CEEC) (pp. 7-10). IEEE.
Hand, D. J., Christen, P., & Kirielle, N. (2021). F*: an interpretable transformation of the F-measure. Machine Learn-ing, 110(3), 451-456.
Homayoun, S., Dehghantanha, A., Ahmadzadeh, M., Hashemi, S., & Khayami, R. (2017). Know abnormal, find evil: fre-quent pattern mining for ransomware threat hunting and intelligence. IEEE transactions on emerging topics in com-puting, 8(2), 341-351.
Hussain, I., Samara, G., Ullah, I., & Khan, N. (2021, December). Encryption for end-user privacy: A cyber-secure smart energy management system. In 2021 22nd International Arab Conference on Information Technology (ACIT) (pp. 1-6). IEEE.
Kulkarni, A., Chong, D., & Batarseh, F. A. (2020). Foundations of data imbalance and solutions for a data democracy. In Data democracy (pp. 83-106). Academic Press.
Li, L., Ma, S., & Zhang, Y. (2014, December). Optimization algorithm based on genetic support vector machine model. In 2014 Seventh International Symposium on Computational Intelligence and Design (Vol. 1, pp. 307-310). IEEE.
Liu, W., Liang, X., & Cui, G. (2020). Common risk factors in the returns on cryptocurrencies. Economic Modelling, 86, 299-305.
Maxmen, A. (2018). AI researchers embrace Bitcoin technology to share medical data. Nature, 555(7697), 293-295.
Musa, N. S., Mirza, N. M., & Ali, A. (2023). Navigating the Complex Landscape of IoT Forensics: Challenges and Emerging Solutions.
Paquet-Clouston, M., Haslhofer, B., & Dupont, B. (2019). Ransomware payments in the bitcoin ecosystem. Journal of Cy-bersecurity, 5(1), tyz003.
Samara, G. (2020, November). Wireless sensor network MAC energy-efficiency protocols: a survey. In 2020 21st Inter-national Arab Conference on Information Technology (ACIT) (pp. 1-5). IEEE.
Samara, G., & Al-okour, M. (2020). Optimal number of cluster heads in wireless sensors networks based on LEACH. arXiv preprint arXiv:2003.13765.
Samara, G., Al-Daoud, E., Swerki, N., & Alzu’bi, D. (2023). The recognition of holy qur’an reciters using the mfccs’ technique and deep learning. Advances in Multimedia, 2023(1), 2642558.
Samara, G., Besani, G. A., Alauthman, M., & Khaldy, M. A. (2020). Energy-efficiency routing algorithms in wireless sensor networks: A survey. arXiv preprint arXiv:2002.07178.
Samara, G., Elhilo, A., Asassfeh, M. R., Injadat, M., Qasem, M. H., Alazaidah, R., & Hnaif, A. A. (2023, December). Op-timizing Road Safety through Intelligent Congestion Management. In 2023 24th International Arab Conference on Information Technology (ACIT) (pp. 1-7). IEEE.
Takeuchi, Y., Sakai, K., & Fukumoto, S. (2018, August). Detecting ransomware using support vector machines. In Workshop Proceedings of the 47th International Conference on Parallel Processing (pp. 1-6).
Vasan, D., Alazab, M., Wassan, S., Naeem, H., Safaei, B., & Zheng, Q. (2020). IMCFN: Image-based malware classifica-tion using fine-tuned convolutional neural network architecture. Computer Networks, 171, 107138.
Yazdinejad, A., HaddadPajouh, H., Dehghantanha, A., Parizi, R. M., Srivastava, G., & Chen, M. Y. (2020). Cryptocurren-cy malware hunting: A deep recurrent neural network approach. Applied Soft Computing, 96, 106630.
Yin, M., Wortman Vaughan, J., & Wallach, H. (2019, May). Understanding the effect of accuracy on trust in machine learning models. In Proceedings of the 2019 chi conference on human factors in computing systems (pp. 1-12).
Yuan, B., Wang, J., Liu, D., Guo, W., Wu, P., & Bao, X. (2020). Byte-level malware classification based on markov im-ages and deep learning. Computers & Security, 92, 101740.
Yuan, Z., Lu, Y., & Xue, Y. (2016). Droiddetector: android malware characterization and detection using deep learn-ing. Tsinghua Science and Technology, 21(1), 114-123.