The increasing number of smartphone over the last few years reflects an impressive growth in the number of advanced malicious applications targeting the smartphone users. Recently, Android has become the most popular operating system opted by users and the most targeted platform for smartphone malware attack. Besides, current mobile malware classification and detection approaches are relatively immature as the new advanced malware exploitation and threats are difficult to be detected. Therefore, an efficient approach is proposed to improve the performance of the mobile malware classification and detection. In this research, a new system call classification with call logs exploitation for mobile attacks has been developed using tokenization approach. The experiment was conducted using static and dynamic-based analysis approach in a controlled lab. System calls with call logs exploitation from 5560 Drebin samples were extracted and further examined. This research paper aims to find the best n value and classifier in classifying the dataset based on the new patterns produced. Na�ve Bayes classifier has successfully achieved accuracy of 99.86% which gives the best result among other classifiers. This new system call classification can be used as a guidance and reference for other researchers in the same field for security against mobile malware attacks targeted to call logs exploitation. � Copyright International Association of Engineers.

The invention of smartphone have made life easier as it is capable of providing important functions used in user's daily life. While different operating system (OS) platform was built for smartphone, Android has become one of the most popular choice. Nonetheless, it is also the most targeted platform for mobile malware attack causing financial loss to the victims. Therefore, in this research, the exploitation on system calls in Android OS platform caused by mobile malware that could lead to financial loss were examined. The experiment was conducted in a controlled lab environment using open source tools by implementing dynamic analysis on 1260 datasets from the Android Malware Genome Project. Based on the experiment conducted, a new system call classification to exploit call logs for mobile attacks has been developed using Covering Algorithm. This new system call classification can be used as a reference for other researcher in the same field to secure against mobile malware attacks by exploiting call logs. In the future, this new system call classification could be used as a basis to develop a new model to detect mobile attacks exploitation via call logs. � 2015 IEEE.

Nowadays 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.