K
Khairul Akram Zainol Ariffin
Researcher at National University of Malaysia
Publications - 27
Citations - 544
Khairul Akram Zainol Ariffin is an academic researcher from National University of Malaysia. The author has contributed to research in topics: Computer science & Digital forensics. The author has an hindex of 7, co-authored 25 publications receiving 188 citations. Previous affiliations of Khairul Akram Zainol Ariffin include Universiti Teknologi Petronas.
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A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis
TL;DR: A semantic and detailed survey of methods used for malware detection like signature-based and heuristic-based, and the importance of memory-based analysis in malware detection is discussed.
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A systematic review on cognitive radio in low power wide area network for industrial IoT applications
Nahla Nurelmadina,Mohammad Kamrul Hasan,Imran Memon,Rashid A. Saeed,Khairul Akram Zainol Ariffin,Elmustafa Sayed Ali,Rania A. Mokhtar,Shayla Islam,Eklas Hossain,Md. Arif Hassan +9 more
TL;DR: The study suggests that the essential factors of design need to be considered to conquer the critical research gaps of the existing LPWAN cognitive-enabled IIoT, and a cognitive low energy architecture is brought to ensure efficient and stable communications in a heterogeneous IIeT.
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Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection
TL;DR: The proposed IHHO can avoid trapping in local optima and has an enhanced search mechanism, relying on mutation, mutation neighborhood search, and rollback strategies to raise the search capabilities and improves population diversity, computational accuracy, and accelerates convergence rate.
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Secure Knowledge and Cluster-Based Intrusion Detection Mechanism for Smart Wireless Sensor Networks
Amjad Mehmood,Akbar Khanan,Muhammad Muneer Umar,Salwani Abdullah,Khairul Akram Zainol Ariffin,Houbing Song +5 more
TL;DR: A knowledge-based context-aware approach for handling the intrusions generated by malicious nodes in WSNs, which operates on a knowledge base, located at the base station, which is used to store the events generated by the nodes inside the network.
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Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis
TL;DR: An integrated malware detection approach that applies memory forensics to extract malicious artifacts from memory and combines them to features extracted during the execution of malware in a dynamic analysis that overcomes the limitation of single path file execution in dynamic analysis.