Work place: University of Technical Malaysia Melaka, Malaysia
E-mail: faizalabdollah@utem.edu.my
Website:
Research Interests: Information Security, Network Architecture, Network Security, Data Structures and Algorithms, Information-Theoretic Security
Biography
Assoc. Prof. Dr. Mohd Faizal Abdollah is currently a senior lecturer in University Teknikal Malaysia Melaka. The research area more focuses on network security, malware detection and network management. In cybersecurity, Dr Mohd Faizal led the sub project under CMERP project with the collaboration with Cyber Security Malaysia. This project more focuses on malware detection, eradication and mitigation. Others than that, Dr Mohd Faizal also involve in various grant sponsor by Ministry of Education, Industrial grant and University grant such as Fundamental Grant for detecting botnet activity, Transdiciplin Grant for detecting the inside threat, ISIF grant for botnet detection using graph theory. He also teaches UTeM course such as Information Technology and IT Security, Network Management and Administration, Advanced Scalable Network and also manage to produce various conference paper and journal in cybersecurity related field.
By Sriyanto Sahib B. Sahrin Abdullah Mohd. Faizal Nanna Suryana Adang Suhendra
DOI: https://doi.org/10.5815/ijmecs.2022.05.03, Pub. Date: 8 Oct. 2022
A range of research procedures have been executed to overcome malware attacks. This research used a malware behavior observe approach on device calls on mobile devices operating gadget kernel. An application used to be mounted on mobile gadget to gather facts and processed them to get dataset. This research used data mining classification approach method and validates it using ten fold cross validation. MiMaLo is a method to normalize a dataset the usage of the min-max aggregate and logarithm function. The application of the MiMaLo method aims to increase the accuracy value. Derived from the experiments, the classifiers overall performance level used to be extensively increasing. The application of the MiMaLo method using the neural network algorithm produces an accuracy of 93.54% with AUC of 0.982.
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