Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Improving Smartphones Battery Life by Reducing Energy Waste of Background Applications

Smartphones have undergone a remarkable evolution over the last few years, from simple calling devices to full fledged computing devices where multiple services and applications run concurrently. Unfortunately, battery capacity increases at much slower pace, resulting as a main bottleneck for Internet connected smartphones. Several software-based techniques have been proposed in the literature for improving the battery life. Most common techniques include data compression, packet aggregation or batch scheduling, offloading partial computations to cloud, switching OFF interfaces (e.g., WiFi or 3G/4G) periodically for short intervals etc. However, there has been no focus on eliminating the energy waste of background applications that extensively utilize smartphone resources such as CPU, memory, GPS, WiFi, 3G/4G data connection etc. In this paper, we propose an Application State Proxy (ASP) that suppresses/stops the applications on smartphones and maintains their presence on any other network device. The applications are resumed/restarted on smartphones only in case of any event, such as a new message arrival. In this paper, we present the key requirements for the ASP service and different possible architectural designs. In short, the ASP concept can significantly improve the battery life of smartphones, by reducing to maximum extent the usage of its resources due to background applications.

Making Social Work Education App’ier : The Process of Developing Information-based Apps for Social Work Education and Practice

Mobile App technology in social work education remains in the embryonic stages of development with a few notable exceptions. The use of Apps in College and University settings has been reported in other sectors of higher education, although there is a paucity of research in relation to its relevance to social work education and practice. The following article describes the creation of four social work education and practice Apps by a team of social work educators. The primary focus is on the design process and the partnership approach to the creation of the tools. It also outlines the rationale for the App development, the working process and the theoretical framework underpinning mobile learning. Furthermore, it provides information on the level of usage of the Apps according to geographical location, download information and time spent on each section of the App. The article also incorporates a pragmatic summary of developmental guidelines which may aid social work educators in the development and implementation of specialist information-based Apps for education and practice.

The Non-Responsive Receiver Problem in Mobile Ad-hoc Networks

This letter exposed a serious unfairness problem with IEEE 802.11 MAC based Mobile Ad-hoc Networks (MANETs) when operating TCP connections, and identifies the three common factors that contribute to this problem. The work initiated the development of a programmable wireless framework that is subsequently used in a spin-out company (TOM), and by the Telecoms Technology Testing centre in Taiwan(Dr D Chieng).

Characterization and Modeling of the Indoor Radio Channel at 868 MHz for a Mobile Bodyworn Wireless Personal Area Network

This letter reports the statistical characterization and modeling of the indoor radio channel for a mobile wireless personal area network operating at 868 MHz. Line of sight (LOS) and non-LOS conditions were considered for three environments: anechoic chamber, open office area and hallway. Overall, the Nakagami-m cdf best described fading for bodyworn operation in 60% of all measured channels in anechoic chamber and open office area environments. The Nakagami distribution was also found to provide a good description of Rician distributed channels which predominated in the hallway. Multipath played an important role in channel statistics with the mean recorded m value being reduced from 7.8 in the anechoic chamber to 1.3 in both the open office area and hallway.