Saudi M.M.Ismail A.A.Ridzuan F.2024-05-282024-05-282018182346902-s2.0-85057117436https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057117436&partnerID=40&md5=72eff52f58659bd9bd0958d279efbb65https://oarep.usim.edu.my/handle/123456789/8889In the cyber world, the trend whereby cybercriminals exploit smartphone vulnerabilities to obtain confidential information or make financial gain is becoming more common. They inject the mobile application with malicious code to exploit the victim�s smartphone. Moreover, they exploit mobile platforms, in particular, the Android operating system, using mobile malware, without the victim�s consent. To combat this problem, this paper presents a new mobile malware call logs classification based on the Android Package Index (API). This new classification is designed to efficiently detect mobile malware attacks. The experiment was conducted in a controlled lab environment by integrating static analysis and Knowledge Data Discovery (KDD) for data cleaning, transformation, and analysis. The dataset is reverse engineered using static analysis. The data consisted of 5,560 samples from Drebin as the training dataset and 500 samples from the Google Play Store for the testing. As a result, thirty-two mobile applications from the Google Play store matched with the proposed classification for call log exploitation. The proposed call log exploitation classification can be used as a reference for other researchers with the same interest and can be further explored as the input for the formation of a mobile malware detection model. � School of Engineering, Taylor�s University.en-USAndroid package indexCall exploitationKnowledge data discoveryMobile malware classificationStatic analysisArticle15216113Special Issue on ICCSIT 2018