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Nor Badrul Anuar
Researcher at Information Technology University
Publications - 164
Citations - 10083
Nor Badrul Anuar is an academic researcher from Information Technology University. The author has contributed to research in topics: Adaptive neuro fuzzy inference system & Computer science. The author has an hindex of 40, co-authored 148 publications receiving 8153 citations. Previous affiliations of Nor Badrul Anuar include University of Malaya & University of Plymouth.
Papers
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The rise of big data on cloud computing
Ibrahim Abaker Targio Hashem,Ibrar Yaqoob,Nor Badrul Anuar,Salimah Binti Mokhtar,Abdullah Gani,Samee U. Khan +5 more
TL;DR: The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced, and research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance.
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Big data
Ibrar Yaqoob,Ibrahim Abaker Targio Hashem,Abdullah Gani,Salimah Binti Mokhtar,Ejaz Ahmed,Nor Badrul Anuar,Athanasios V. Vasilakos +6 more
TL;DR: This paper presents a comprehensive discussion on state-of-the-art big data technologies based on batch and stream data processing based on structuralism and functionalism paradigms and strengths and weaknesses of these technologies are analyzed.
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The role of big data in smart city
Ibrahim Abaker Targio Hashem,Victor Chang,Nor Badrul Anuar,Kayode S. Adewole,Ibrar Yaqoob,Abdullah Gani,Ejaz Ahmed,Haruna Chiroma +7 more
TL;DR: The state-of-the-art communication technologies and smart-based applications used within the context of smart cities are described and a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified.
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Evaluation of machine learning classifiers for mobile malware detection
TL;DR: An alternative solution to evaluating malware detection using the anomaly-based approach with machine learning classifiers is proposed, which revealed that the k-nearest neighbor classifier efficiently detected the latest Android malware with an 84.57 % true-positive rate higher than other classifiers.
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The Evolution of Android Malware and Android Analysis Techniques
TL;DR: A comprehensive survey on leading Android malware analysis and detection techniques, and their effectiveness against evolving malware, is presented and categorizes systems by methodology and date to evaluate progression and weaknesses.