Mobile malware is ubiquitous in many malicious activities such as money stealing. Consumers are charged without their consent. This paper explores how mobile malware exploit the system calls via SMS. As a solution, we proposed a system calls classification based on surveillance exploitation system calls for SMS. The proposed system calls classification is evaluated and tested using applications from Google Play Store. This research focuses on Android operating system. The experiment was conducted using Drebin dataset which contains 5560 malware applications. Dynamic analysis was used to extract the system calls from each application in a controlled lab environment. This research has developed a new mobile malware classification for Android smartphone using a covering algorithm. The classification has been evaluated in 500 applications and 126 applications have been identified to contain malware. � Springer International Publishing AG 2017.

Android mobile devices are used for various applications. Online banking and shopping are increasingly being performed on smartphones. As the role of smartphones in business grows, the floodgates have opened mobile devices to malware threats, which can be exploited for malicious purposes. Mobile malware is growing in sophistication and continues to target consumers. Consumers are charged without affirmative consent. As a solution to this challenge, we proposed a system call classification based on surveillance exploitation system calls for SMS. The proposed system calls classification is evaluated and tested using applications from Google Play Store. This research focuses on Android operating system. The experiment was conducted using Drebin dataset which contains 5560 malware applications. Dynamic analysis was used to extract the system calls from each application in a controlled lab environment. This research has developed a new mobile malware classification for Android smartphone using a covering algorithm. The classification has been evaluated in 500 applications and 126 applications have been identified to contain malware. � 2017 American Scientific Publishers All rights reserved.

The Quran covers all aspects of our life and usually referred, as a book of knowledge. The extraction of Quranic knowledge is a difficult task, as the Quran is rich in its linguistics and multi-layered meanings, difficult if done without the use of other resources such as the Hadith and Tafsir by Muslim Scholars. Several Quranic ontologies have been developed in recent years, however most of these ontologies only focus on certain domains and language rather than contents of the Quran. Hence this paper will describe the extraction of the knowledge from the Tafsir performed by Muslim Scholars that enables users to search and retrieve relevance information based on the Quran ontology that includes several fields like economy, health and others. This process is carried out to extract the components of ontology such as the concept, relation and rules. The extraction is performed based on search of the desired keyword (as a single word or multiple phrases with logical operator), search weightage (rank) or keyword expansion from the domain knowledge based that enables for further navigate related information from the search result. 2017 American Scientific Publishers All rights reserved.

Ontology is used as knowledge representation of a particular domain that consists of the concepts and the two relations, namely taxonomic relation and non-taxonomic relation. In ontology, both relations are needed to give more knowledge about the domain texts, especially the non-taxonomic components that used to describe more about that domain. Most existing extraction methods extract the non-taxonomic relation component that exists in a same sentence with two concepts. However, there is a possibility of missing or unsure concept in a sentence, known as an incomplete sentence. It is difficult to identify the matching concepts in this situation. Therefore, this paper presents a method, namely similarity extraction method (SEM) to identify a missing concept in a non-taxonomic relation by using a rough set theory. The SEM will calculate the similarity precision and suggest as much as similar or relevant concepts to replace the missing or unclear value in an incomplete sentence. Data from the Tourism Corpus has been used for the experiment and the results were then evaluated by the domain experts. It is believed that this work is able to increase the pair extraction and thus enrich the domain texts. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.