MBotCS: A mobile botnet detection system based on machine learning

As the use of mobile devices spreads dramatically, hackers have started making use of mobile botnets to steal user information or perform other malicious attacks. To address this problem, in this paper we propose a mobile botnet detection system, called MBotCS. MBotCS can detect mobile device traffic indicative of the presence of a mobile botnet based on prior training using machine learning techniques. Our approach has been evaluated using real mobile device traffic captured from Android mobile devices, running normal apps and mobile botnets. In the evaluation, we investigated the use of 5 machine learning classifier algorithms and a group of machine learning box algorithms with different validation schemes. We have also evaluated the effect of our approach with respect to its effect on the overall performance and battery consumption of mobile devices

Dynamic Learning Media to Improve the Pedagogic Experience in Conveying of SCM Course

Abstract This paper presents the application of free/open source software (FOSS) for developing educational in Supply Chain Management (SCM) course. There were abundant educational tools based F applications. However, lecturer still faces problems to implement such an learning media for impro the pedagogic experience i.e. customizing of software function, developing of a specific educat media, and illustrating of a SCM course content. The purpose of this research is to design dyn learning media for increasing efficiency in conveying subject matter of SCM course. We combin mobile and/or web devices with FOSS of web service to design dynamic learning media. It has a mo of real distribution problem in commodity paddy was captured. We design an interactive Web-based Mobile-based application by using WSDL, PHP and My SQL, and SOAP. The result of the resea Mobile & Web SCM application as dynamic learning media- will be able to improve the pedag experience to students. Keywords: Educational tool, FOSS, dynamic learning media, SCM course, the pedagogic approach

Mobile Devices for Arabic Learning in Junior High Schools: The Teachers’ Perspective

The present study aims to identify teachers' perceptions of mobile devices use in Arabic learning at junior high schools. Employed a qualitative method with a case study, 24 Arabic language teachers from various junior high schools in the South Tangerang City involved in this study selected by using a random sampling method. Based on the teacher perspectives, the results of the study reveal that the use of mobile devices facilitates students in understanding Arabic lessons and increase their motivation for learning. Time limitations and large classroom sizes were obstacles that decrease teachers’ interest to integrate mobile devices into classroom activities. Reading is by far the main language skill taught by teachers and there is a less focus on productive skills such as speaking and writing. The students have less attention and motivation for learning Arabic while the teachers have attempted to deal with this problem by several measures.The characteristic of Arabic language differs from each context. However, whether for inside or outside classroom activities, active methods are of particular importance for Arabic learning. Arabic teaching and learning practices facilitated by mobile devices require careful planning for a successful implementation lead to more active and effective learning of Arabic

Mobile web and app QoE monitoring for ISPs - from encrypted traffic to speed index through machine learning

International audienceWeb browsing is one of the key applications of the Internet. In this paper, we address the problem of mobile Web and App QoE monitoring from the Internet Service Provider (ISP) perspective, relying on in-network, passive measurements. Our study targets the analysis of Web and App QoE in mobile devices, including mobile browsing in smartphones and tablets, as well as mobile apps. As a proxy to Web QoE, we focus on the analysis of the well-known Speed Index (SI) metric. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI of individual web page and app loading sessions, using as input only packet level data. Empirical evaluations on a large, multi mobile-device corpus of Web and App QoE measurements for top popular websites and selected apps demonstrate that the proposed solution can properly infer the SI from in-network, encrypted-traffic measurements, relying on learning-based models. Our study also reveals relevant network and web page content characteristics impacting Web QoE in mobile devices, providing a complete overview on the mobile Web and App QoE assessment problem

Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission

By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, which enable intelligent mobile applications. In this emerging research area, one key direction is to efficiently utilize radio resources for wireless data acquisition to minimize the latency of executing a learning task at an edge server. Along this direction, we consider the specific problem of retransmission decision in each communication round to ensure both reliability and quantity of those training data for accelerating model convergence. To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed. Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training. Underpinning the proposed protocol is a derived elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This relation facilitates the design of a simple threshold based policy for importance ARQ. The policy is first derived based on the classic classifier model of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. The policy is then extended to the more complex model of convolutional neural networks (CNN) where data uncertainty is measured by entropy. Extensive experiments have been conducted for both the SVM and CNN using real datasets with balanced and imbalanced distributions. Experimental results demonstrate that importance ARQ effectively copes with channel fading and noise in wireless data acquisition to achieve faster model convergence than the conventional channel-aware ARQ.Comment: This is an updated version: 1) extension to general classifiers; 2) consideration of imbalanced classification in the experiments. Submitted to IEEE Journal for possible publicatio

Integrating Game Engines into the Mobile Cloud as Micro-services

Game engines have been widely adopted in fields other than games, such as data visualization and game-based education. As the number of mobile devices owned by each person increases, extra resources are available in personal device clouds, expanding typical learning space to outside of the classroom and increasing possibilities for teacher-student interactions. Owning multiple devices poses the problem of how to make use of idle resources on devices that are slightly dated or lack portability compared to newer models. Such resources include CPU power, display, and data storage. In order to solve this problem, an architecture is proposed for mobile applications to access these resources on various mobile devices. The main approach used here is to divide an application into several modules and distribute them over a personal device cloud (formed by same-user-owned devices) as micro-services. In this architecture, game engines will be incorporated as a render module to tap in its rendering capability. Additionally, modules will communicate using CoAP which has minimal overhead. To evaluate the feasibility of such architecture, a prototype is implemented and deployed over a mobile device, and tested in a modest context that is similar to real life settings

What is motivating engineering students to use 1:1 mobile devices for learning?

Context: Engineering education is embarking on a new journey, where curriculum designers are needing evidence based research to understand how the use of 1:1 mobile devices can influence student perceptions of self-directed learning, to improve curriculum engagement and to measurable assessment outcomes. Using student perceptions of learning motivation and learning intervention theory, higher order thinking skills can be encouraged in problem-based learning. Understanding what is motivating students to learn when and using 1:1 mobile devices will help curriculum designers and facilitators to engage students.Purpose: This paper will explore survey results of a recent study, revealing how students are responding to a self-directed curriculum design where engineering students are accessing learning interventions to enhance their learning experience when using 1:1 mobile device technology.Approach: This study approach is focusing on answering the following questions:How do engineering students use their 1:1 mobile devices for self-directed learning?What learning motivation perceptions do engineering students have when they are making choices about using a 1:1 mobile device for their learning?How does using a 1:1 mobile device translate into improved learning outcomes?How does the improved learning outcomes foster a culture of innovation?Finding answers to these questions is important for this study to provide academic rigour identify learning motivations that may prove useful for curriculum designers who are interested in developing self-directed learning pathways. It is accepted, technology is a student directed and socially accepted variable that is proving to be an important \u27value-add\u27 to the learning spaces of higher education students.Results: An analysis has shown that student motivation is influenced by motivation variables when curriculum interventions are used in engineering courses. The data suggests that student perceptions of learning motivations can be an important factor to influence the level of engagement when using 1:1 devices for learning. Curriculum designers and course facilitators may benefit from implementing selective interventions with the aim to encourage higher order thinking when problem solving.Conclusions: Engineering curriculum designers will benefit from this research when considering how best to implement 1:1 mobile devices as a motivating factor for learning. Learning interventions are an important factor in influencing student perceptions of motivation to learn when using 1:1 mobile devices

Informal Learning Evidence in Online Communities of Mobile Device Enthusiasts

This paper describes a study that investigated the informal learning practices of enthusiastic mobile device owners. Informal learning is far more widespread than is often realised. Livingston (2000) pointed out that Canadian adults spend an average of 15 hours per week on informal learning activities, more than they spend on formal learning activities. The motivation for these learning efforts generally comes from the individual, not from some outside force such as a school, university or workplace. Therefore, in the absence of an externally imposed learning framework, informal learners will use whatever techniques, resources and tools best suit their learning needs and personal preferences. As ownership of mobile technologies becomes increasingly widespread in the western world, it is likely that learners who have access to this technology will use it to support their informal learning efforts. This paper presents the findings of a study into the various and innovative ways in which PDA and Smartphone users exploit mobile device functionality in their informal learning activities. Vavoula (2004) highlighted some of the difficulties inherent in researching informal learning; it can be intentional or unintentional and people may even be unaware that any learning has taken place. There is also the practical problem of locating a pool of mobile device users who not only engage in mobile informal learning, but who are also willing to provide information about their activities. PDA and Smartphone enthusiasts were targetted as the community most likely to be using their devices in informal learning and participants were recruited from the active community of web forum users. Web forums are internet-based, asynchronous discussion groups that are aimed at people who share a specific interest; in this case, mobile devices. Messages were posted in the forums inviting members to participate in a web survey on informal learning with mobile devices. This approach was successful, generating over 200 responses of which over 100 described informal learning with mobile devices. The findings suggested that mobile device users deploy the mobile, connective and collaborative capabilities of their devices in a variety of informal learning contexts, in quite innovative ways. Trends emerged, such as the increasing importance of podcasting and audio, which may have implications for future studies. Informal learners identified learning activities that could be enhanced by the involvement of mobile technology, and developed methods and techniques that helped them achieve their learning goals. This paper describes the methods used in the study and discusses the results, locating them in the context of the wider literature on informal learning. It explores key issues, such as participation in collaborative informal learning, that emerged from the findings and outlines research directions arising from the study. with mobile devices. This approach was successful, generating over 200 responses of which over 100 described informal learning with mobile devices.Open University, United Kingdo