Ahmad I.N.Ridzuan F.Saudi M.M.2024-05-282024-05-2820171936661210.1166/asl.2017.89672-s2.0-85023775541https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023775541&doi=10.1166%2fasl.2017.8967&partnerID=40&md5=40b395931e1bfd992b8d2a758bb1a192https://oarep.usim.edu.my/handle/123456789/9291Nowadays it becomes harder for malware analyst to detect malwares efficiently especially with the growth of data. Therefore, further research needs to be carried out to improve the malwares detection performance. In this paper, an in-depth study on the existing indexing rule or methods used for malware detection and classification is further discussed and evaluated. Furthermore, this paper also proposes a new indexing algorithm by using ngram to optimize mobile malware detection performance and focusing on the accuracy and speed. The indexing rule uses three sub-process that utilizes n-gram algorithm to shorten the string length patterns and thus optimize the mobile malware detection speed and accuracy. This paper can be used as guidance for other malware analysts or researchers with the same interest. � 2017 American Scientific Publishers All rights reserved.en-USIndexing mobile malwareMobile malware detectionN-gram algorithmArticle46274630235