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您的检索式：作者名="Abdulrahman Alreshidi"
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| 1 | Mining Patterns from Change Logs to Support Reuse-Driven Evolution of Software Architectures显示文摘Modern software systems are subject to a continuous evolution under frequently varying requirements andchanges in systems' operational environments. Lehman's law of continuing change demands for long-living and continuouslyevolving software to prolong its productive life and economic value by accommodating changes in existing software. Reusableknowledge and practices have proven to be successful for continuous development and evolution of the software effectivelyand efficiently. However, challenges such as empirical acquisition and systematic application of the reusable knowledge andpractices must be addressed to enable or enhance software evolution. We investigate architecture change logs -- mininghistories of architecture-centric software evolution -- to discover change patterns that 1) support reusability of architecturalchanges and 2) enhance the efficiency of the architecture evolution process. We model architecture change logs as a graphand apply graph-based formalism (i.e., graph mining techniques) to discover software architecture change patterns. Wehave developed a prototype that enables tool-driven automation and user decision support during software evolution. Wehave used the ISO-IEC-9126 model to qualitatively evaluate the proposed solution. The evaluation results suggest that theproposed solution 1) enables the reusability of frequent architectural changes and 2) enhances the efficiency of architecture-centric software evolution process. The proposed solution promotes research efforts to exploit the history of architecturalchanges to empirically discover knowledge that can guide architecture-centric software evolution. | Aakash Ahlnad Claus Pahl Ahmed B. Altamimi Abdulrahman Alreshidi | 2018 | Journal of Computer Science & Technology2018，33，6： | 0 |
| 2 | Phishing Websites Detection by Using Optimized Stacking Ensemble Model显示文摘Phishing attacks are security attacks that do not affect only individuals’or organizations’websites but may affect Internet of Things(IoT)devices and net-works.IoT environment is an exposed environment for such attacks.Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users.Machine and deep learning and other methods were used to design detection methods for these attacks.However,there is still a need to enhance detection accuracy.Optimization of an ensemble classification method for phishing website(PW)detection is proposed in this study.A Genetic Algo-rithm(GA)was used for the proposed method optimization by tuning several ensemble Machine Learning(ML)methods parameters,including Random Forest(RF),AdaBoost(AB),XGBoost(XGB),Bagging(BA),GradientBoost(GB),and LightGBM(LGBM).These were accomplished by ranking the optimized classi-fiers to pick out the best classifiers as a base for the proposed method.A PW data-set that is made up of 4898 PWs and 6157 legitimate websites(LWs)was used for this study's experiments.As a result,detection accuracy was enhanced and reached 97.16 percent. | Zeyad Ghaleb Al-Mekhlafi Badiea Abdulkarem Mohammed Mohammed Al-Sarem Faisal Saeed Tawfik Al-Hadhrami Mohammad T.Alshammari Abdulrahman Alreshidi Talal Sarheed Alshammari | 2022 | Computer Systems Science & Engineering2022，41，4： | 0 |
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