How to cite this paper
Al-Khateeb, M., Al-Mousa, M., Al-Sherideh, A., Almajali, D., Asassfeha, M & Khafajeh, H. (2023). Awareness model for minimizing the effects of social engineering attacks in web applications.International Journal of Data and Network Science, 7(2), 791-800.
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An, C., Lim, H., Kim, D. W., Chang, J. H., Choi, Y. J., & Kim, S. W. (2020). Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study. Sci Rep, 10(1).
Buldas, A., Gadyatskaya, O., Lenin, A., Mauw, S., & Trujillo-Rasua, R. (2020). Attribute evaluation on attack trees with incomplete information. Computers & Security, 88, 101630.
Bunder, M., Nitaj, A., Susilo, W., & Tonien, J. (2018). Cryptanalysis of RSA-type cryptosystems based on Lucas sequenc-es, Gaussian integers and elliptic curves. Journal of Information Security and Applications, 40, 193-198.
Chen, Y., & Zhou, Y. (2020). Machine learning based decision making for time varying systems: Parameter estimation and performance optimization. Knowledge-Based Systems, 190, 105479.
Chin, W.-L., Li, W., & Chen, H.-H. (2017). Energy Big Data Security Threats in IoT-Based Smart Grid Communications. IEEE Communications Magazine, 55(10), 70-75.
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He, X., Xu, L., & Cha, C. (2018). Malicious javascript Code Detection Based on Hybrid Analysis. Paper presented at the 2018 25th Asia-Pacific Software Engineering Conference (APSEC).
Hosseini, N., Fakhar, F., Kiani, B., & Eslami, S. (2019). Enhancing the security of patients’ portals and websites by detect-ing malicious web crawlers using machine learning techniques. International journal of medical informatics, 132, 103976.
Liu, J., Xu, M., Wang, X., Shen, S., & Li, M. (2018). A Markov Detection Tree-Based Centralized Scheme to Automati-cally Identify Malicious Webpages on Cloud Platforms. IEEE Access, 6, 74025-74038.
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for in-ternet of things data analysis: a survey. Digital Communications and Networks, 4(3), 161-175.
Paananen, H., Lapke, M., & Siponen, M. (2020). State of the art in information security policy development. Computers & Security, 88.
Rasmi, M., & Al-Qawasmi, K. E. (2016). Improving Analysis Phase in Network Forensics by Using Attack Intention Analysis. International Journal of Security and Its Applications, 10(5), 297-308.
Sadeghi, A.-R., Wachsmann, C., & Waidner, M. (2015). Security and privacy challenges in industrial internet of things. Paper presented at the Proceedings of the 52nd Annual Design Automation Conference on - DAC '15.
Sahoo, S. R., & Gupta, B. B. (2019). Classification of various attacks and their defence mechanism in online social net-works: a survey. Enterprise Information Systems, 13(6), 832-864.
Saxe, J., Harang, R., Wild, C., & Sanders, H. (2018). A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content. Paper presented at the 2018 IEEE Security and Privacy Workshops (SPW).
Silva, C. M. R. d., Feitosa, E. L., & Garcia, V. C. (2020). Heuristic-based strategy for Phishing prediction: A survey of URL-based approach. Computers & Security, 88.
Singh, A., Parizi, R. M., Zhang, Q., Choo, K.-K. R., & Dehghantanha, A. (2020). Blockchain smart contracts formaliza-tion: Approaches and challenges to address vulnerabilities. Computers & Security, 88.
Singh, K. J., & De, T. (2017). MLP-GA based algorithm to detect application layer DDoS attack. Journal of Information Security and Applications, 36, 145-153.
Wang, H.-h., Yu, L., Tian, S.-w., Peng, Y.-f., & Pei, X.-j. (2019). Bidirectional LSTM Malicious webpages detection algo-rithm based on convolutional neural network and independent recurrent neural network. Applied Intelligence, 49(8), 3016-3026.
Wang, R., Zhu, Y., Tan, J., & Zhou, B. (2017). Detection of malicious web pages based on hybrid analysis. Journal of In-formation Security and Applications, 35, 68-74.
Wang, Z., Feng, X., Niu, Y., Zhang, C., & Su, J. (2017). TSMWD: A High-Speed Malicious Web Page Detection System Based on Two-Step Classifiers. Paper presented at the 2017 International Conference on Networking and Network Ap-plications (NaNA).
Xu, X., Zhao, Z., Xu, X., Yang, J., Chang, L., Yan, X., & Wang, G. (2020). Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven models. Knowledge-Based Systems, 190.
Yasasin, E., Prester, J., Wagner, G., & Schryen, G. (2020). Forecasting IT security vulnerabilities – An empirical analysis. Computers & Security, 88.
Aliero, M. S., & Ghani, I. (2015). A Component Tool IEEE Access.
Aliero, M. S., Ghani, I., Qureshi, K. N., & Rohani, M. F. a. (2019). An algorithm for detecting SQL injection vulnerability using black-box testing. Journal of Ambient Intelligence and Humanized Computing, 11(1), 249-266.
Almasalha, F., Naït-Abdesselam, F., Trajcevski, G., & Khokhar, A. (2018). Secure transmission of multimedia contents over low-power mobile devices. Journal of Information Security and Applications, 40, 183-192.
Al-Mousa, M. R. (2021). Analyzing Cyber-Attack Intention for Digital Forensics Using Case-Based Reasoning. arXiv pre-print arXiv:2101.01395.
Al-Mousa, M. R. (2021, July). Generic Proactive IoT Cybercrime Evidence Analysis Model for Digital Forensics. In 2021 International Conference on Information Technology (ICIT) ( 654-659). IEEE.
Al-Mousa, M. R., Al Zaqebah, M ., Al-Sherideh, A. S., Al-Gghanim, Mohammed., Samara, G., Al-Matarneh, S., Asassfeh, M, R. (2022). Examining Digital Forensic Evidence for Android Applications. In 2022 23nd International Arab Con-ference on Information Technology (ACIT).
Al-Mousa, M. R., Sweerky, N. A., Samara, G., Alghanim, M., Hussein, A. S. I., & Qadoumi, B. (2021, December). General Countermeasures of Anti-Forensics Categories. In 2021 Global Congress on Electrical Engineering (GC-ElecEng) (pp. 5-10). IEEE.
Alqahtani, F. H., & Alsulaiman, F. A. (2020). Is image-based CAPTCHA secure against attacks based on machine learn-ing? An experimental study. Computers & Security, 88, 101635.
Al-Sherideh, A. S., Ismail, R. (2020). Motivating path between security and privacy factors on the actual use of mobile government applications in Jordan. International Journal on Emerging Technologies, 11(5), 558-566
Al-Sherideh, A. S., Ismail, R., Wahid, F. A., Fabil, N., & Ismail, W. (2018). Mobile government applications based on se-curity and privacy: a literature review. International Journal of Engineering and Technology (UAE). https://oarep.usim.edu.my/jspui/handle/123456789/1614
An, C., Lim, H., Kim, D. W., Chang, J. H., Choi, Y. J., & Kim, S. W. (2020). Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study. Sci Rep, 10(1).
Buldas, A., Gadyatskaya, O., Lenin, A., Mauw, S., & Trujillo-Rasua, R. (2020). Attribute evaluation on attack trees with incomplete information. Computers & Security, 88, 101630.
Bunder, M., Nitaj, A., Susilo, W., & Tonien, J. (2018). Cryptanalysis of RSA-type cryptosystems based on Lucas sequenc-es, Gaussian integers and elliptic curves. Journal of Information Security and Applications, 40, 193-198.
Chen, Y., & Zhou, Y. (2020). Machine learning based decision making for time varying systems: Parameter estimation and performance optimization. Knowledge-Based Systems, 190, 105479.
Chin, W.-L., Li, W., & Chen, H.-H. (2017). Energy Big Data Security Threats in IoT-Based Smart Grid Communications. IEEE Communications Magazine, 55(10), 70-75.
Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and win-dow-opening behaviours in smart buildings. Energy and Buildings, 223. doi:10.1016/j.enbuild.2020.110159.
He, X., Xu, L., & Cha, C. (2018). Malicious javascript Code Detection Based on Hybrid Analysis. Paper presented at the 2018 25th Asia-Pacific Software Engineering Conference (APSEC).
Hosseini, N., Fakhar, F., Kiani, B., & Eslami, S. (2019). Enhancing the security of patients’ portals and websites by detect-ing malicious web crawlers using machine learning techniques. International journal of medical informatics, 132, 103976.
Liu, J., Xu, M., Wang, X., Shen, S., & Li, M. (2018). A Markov Detection Tree-Based Centralized Scheme to Automati-cally Identify Malicious Webpages on Cloud Platforms. IEEE Access, 6, 74025-74038.
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for in-ternet of things data analysis: a survey. Digital Communications and Networks, 4(3), 161-175.
Paananen, H., Lapke, M., & Siponen, M. (2020). State of the art in information security policy development. Computers & Security, 88.
Rasmi, M., & Al-Qawasmi, K. E. (2016). Improving Analysis Phase in Network Forensics by Using Attack Intention Analysis. International Journal of Security and Its Applications, 10(5), 297-308.
Sadeghi, A.-R., Wachsmann, C., & Waidner, M. (2015). Security and privacy challenges in industrial internet of things. Paper presented at the Proceedings of the 52nd Annual Design Automation Conference on - DAC '15.
Sahoo, S. R., & Gupta, B. B. (2019). Classification of various attacks and their defence mechanism in online social net-works: a survey. Enterprise Information Systems, 13(6), 832-864.
Saxe, J., Harang, R., Wild, C., & Sanders, H. (2018). A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content. Paper presented at the 2018 IEEE Security and Privacy Workshops (SPW).
Silva, C. M. R. d., Feitosa, E. L., & Garcia, V. C. (2020). Heuristic-based strategy for Phishing prediction: A survey of URL-based approach. Computers & Security, 88.
Singh, A., Parizi, R. M., Zhang, Q., Choo, K.-K. R., & Dehghantanha, A. (2020). Blockchain smart contracts formaliza-tion: Approaches and challenges to address vulnerabilities. Computers & Security, 88.
Singh, K. J., & De, T. (2017). MLP-GA based algorithm to detect application layer DDoS attack. Journal of Information Security and Applications, 36, 145-153.
Wang, H.-h., Yu, L., Tian, S.-w., Peng, Y.-f., & Pei, X.-j. (2019). Bidirectional LSTM Malicious webpages detection algo-rithm based on convolutional neural network and independent recurrent neural network. Applied Intelligence, 49(8), 3016-3026.
Wang, R., Zhu, Y., Tan, J., & Zhou, B. (2017). Detection of malicious web pages based on hybrid analysis. Journal of In-formation Security and Applications, 35, 68-74.
Wang, Z., Feng, X., Niu, Y., Zhang, C., & Su, J. (2017). TSMWD: A High-Speed Malicious Web Page Detection System Based on Two-Step Classifiers. Paper presented at the 2017 International Conference on Networking and Network Ap-plications (NaNA).
Xu, X., Zhao, Z., Xu, X., Yang, J., Chang, L., Yan, X., & Wang, G. (2020). Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven models. Knowledge-Based Systems, 190.
Yasasin, E., Prester, J., Wagner, G., & Schryen, G. (2020). Forecasting IT security vulnerabilities – An empirical analysis. Computers & Security, 88.