The target of botnet attacks has shifted from the personal computers to smartphones and mobile devices due to computational power and functionality of the mobile devices. Mobile botnet is a network consists of compromised mobile devices controlled by a botmaster through a command and control (C&C) network. Nowadays mobile botnets attacks are increasingly being used for advanced political or financial interest. Due to its popularity amongst the mobile operating system, Android has become the most targeted platform by the mobile botnets. The popularity of Android attracts the attackers to develop malicious applications with the botnet capability to hijack users� devices. In this paper, a new Android botnet classification based on GPS exploitation based on permissions and API calls is proposed using feature selection. The training was carried out using malware dataset from the Drebin and tested using 800 mobile apps from the Google Play store. The experiment was conducted using static analysis and open source tools in a controlled lab environment. This new classification can be used as a reference for other researchers in the same field to secure against GPS exploitation from Android botnet attacks. � Springer International Publishing AG 2018.

Currently, mobile botnet attacks have shifted from computers to smartphones due to its functionality, ease to exploit, and based on financial intention. Mostly, it attacks Android due to its popularity and high usage among end users. Every day, more and more malicious mobile applications (apps) with the botnet capability have been developed to exploit end users' smartphones. Therefore, this paper presents a new mobile botnet classification based on permission and Application Programming Interface (API) calls in the smartphone. This classification is developed using static analysis in a controlled lab environment and the Drebin dataset is used as the training dataset. 800 apps from the Google Play Store have been chosen randomly to test the proposed classification. As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. This new classification can be used as the input for mobile botnet detection for future work, especially for financial matters. � 2017 IEEE.

A study of Corporate Social Responsibility (CSR) has become the subject of research for many academias in the field of business studies. Despite increasing attention has been accorded to CSR in developed countries, very few is known of the research of CSR in developing multicultural, multiethnic country like Malaysia. With about 27.5 million people, Malaysia is made up of three main ethnic populations, namely; Malays, Chinese and Indians. The fact that Malaysia has diverging ethnic groups and cultural system, this study addresses an insight analysis of CSR from a different perspective. The study seeks to find out the level of CSR understanding of the Malaysian executives in the financial services sector across different ethnic groups. A total of 376 responses were collected and analysed in this study. The questionnaire was used to measure the perceptions of the respondents towards several CSR issues. The findings across ethnicity of the respondents revealed that in general, there is no homogenous consensus. The finding clearly demonstrates differences of perceptions among Malay, Chinese and Indian respondents. This is potentially a significant finding, since culture gives a significant impact on people attitude, behaviour and perception. � Medwell Journals, 2013.

Corporate social responsibility (CSR) agenda has increasingly come under the spotlight over cases of unethical business practices by public and private companies around the world. Shareholders and members of the general public are being confronted with a steady stream of corporate scandal that has an increasingly deteriorating effect on investors' confidence (Miller, 2009). In fact, the importance of CSR commitments and initiatives may have a direct impact on the success of the future business atmosphere as many organisations worldwide have come under pressure to comply with the international CSR standards and practices (Miller, 2009). In order to demonstrate the commitment to achieve economic advancement, Malaysian government is keen to incorporate CSR programmes into companies' agenda to heighten their profile, so that they can gain recognition and increase investors' confidence from the perspective of local and international investors. Therefore, this paper seeks to measure the level of CSR commitment by financial sector in Malaysia. 376 respondents selected from financial executives were asked to indicate their companies' level of commitment towards 17 elements of CSR. Overall, the findings indicate that financial services sector in Malaysia promotes important roles in community and environment, culture and philanthropy.

The popularity and adoption of Android smartphones has attracted malware authors to spread the malware to smartphone users. The malware on smartphone comes in various forms such as Trojans, viruses, worms and mobile botnet. However, mobile botnet or Android botnet are more dangerous since they pose serious threats by stealing user credential information, distributing spam and sending distributed denial of service (DDoS) attacks. Mobile botnet is defined as a collection of compromised mobile smartphones and controlled by a botmaster through a command and control (C & C) channel to serve a malicious purpose. Current research is still lacking in terms of their low detection rate due to their selected features. It is expected that a hybrid analysis could improve the detection rate. Therefore, machine learning methods and hybrid analysis which combines static and dynamic analyses were used to analyse and classify system calls, permission and API calls. The objective of this paper is to leverage machine learning techniques to classify the Android applications (apps) as botnet or benign. The experiment used malware dataset from the Drebin for the training and mobile applications from Google Play Store for testing. The results showed that Random Forest Algorithm achieved the highest accuracy rate of 97.9%. In future, more significant approach by using different feature selection such as intent, string and system component will be further explored for a better detection and accuracy rate.

The objective of this paper is to explore the level of understanding of the Malaysian Muslim managers in financial sector towards corporate social responsibility (CSR) issues and practices. A total of 243 responses were analysed in different segments of financial institutions in Malaysia. The questionnaire was used to measure the perceptions and awareness of the respondents towards CSR concept and commitment. The results indicate that, in general, Muslim managers demonstrate a growing concern towards CSR practices in Malaysia. However, the result from factor analysis reveals that Muslim managers perceived public maslahah and religious activities as the most important CSR commitment. The findings provide useful implication for the effective and efficient management of CSR across financial services sector in Malaysia.