Abstract-Mobile app reviews are gaining importance as a crowd source to improve the quality of mobile apps. Mobile app review systems are providing a platform for users to share their experiences and to support in decision making for a certain app. Developers, on the other side, are utilizing the review system to get real-life user experience as a source of improving their apps. This paper has analyzed existing review system and proposed few recommendations for the current review system to improve the quality of app reviews. The proposed review system can help for collection and analysis of user reviews to make it more meaningful with less intensive data mining techniques. The proposed system can help the end users to get an overview of mobile apps. The recommendations in this paper are derived from the existing literature related to app reviews and will help to improve the current review systems for better app reviews from users as well as developers perspective.

Abstract-Crowdsourcing is a famous technique to get innovative ideas and soliciting contribution from a large online community particularly in e-business. This technique is contributing towards changing the current business techniques and practices. It is also equally famous in analysis and design of m-business services. Mobile app stores are providing an opportunity for its users' to participate and contribute in the growth of mobile app market. App reviews given by users usually contain active, heterogeneous and real life user experience of mobile app which can be useful to improve the quality of app. Best to our knowledge, the strength of mobile app reviews as a crowdsource is not fully recognized and understood by the community yet. In this paper, we have analysed a crowdsourcing reference model to find out which features of crowdsource are present and are related to our case of mobile app reviews as a crowdsource. We have analyzed and discussed each construct of the reference model from the perspective of mobile app reviews. Moreover, app reviews as a crowdsourcing technique is discussed by utilizing the four pillars of the reference model: the crowd, the crowdsourcer, the crowdsourcing, and the crowdsourcing platform. We have also identified and partially validated certain constructs of the model and highlighted the significance of app reviews as a crowdsource based on existing literature. In this study, only one crowdsourcing reference model is used which can be a limitation of our study. The study can be further investigated and compared with other crowdsourcing reference models to get better insights of app reviews as a crowdsource. We believe that the understanding of app reviews as a crowdsourcing technique can lead to the further development of the mobile app market and can open further research opportunities.

In Today's Digital World, the continuous interruption of users has affected Web Servers (WSVRs), through Distributed Denial-of-Service (DDoS) attacks. These attacks always remain a massive warning to the World Wide Web (WWW). These warnings can interrupt the accessibility of WSVRs, completely by disturbing each data processing before intercommunication properties over pure dimensions of Data-Driven Networks (DDN), management and cooperative communities on the Internet technology. The purpose of this research is to find, describe and test existing tools and features available in Linux-based solution lab design Availability Protection System (Linux-APS), for filtering malicious traffic flow of DDoS attacks. As source of malicious traffic flow takes most widely used DDoS attacks, targeting WSVRs. Synchronize (SYN), User Datagram Protocol (UDP) and Internet Control Message Protocol (ICMP) Flooding attacks are described and different variants of the mitigation techniques are explained. Available cooperative tools for manipulating with network traffic, like; Ebtables and Iptables tools are compared, based on each type of attacks. Specially created experimental network was used for testing purposes, configured filters servers and bridge. Inspected packets flow through Linux-kernel network stack along with tuning options serving for increasing filter server traffic throughput. In the part of contribution as an outcomes, Ebtables tool appears to be most productive, due to less resources it needed to process each packet (frame). It is pointed out that separate detecting system is needed for this tool, in order to provide further filtering methods with data. As main conclusion, Linux-APS, solutions provide full functionality for filtering malicious traffic flow of DDoS attacks either in standalone state or combined with detecting systems.

This article describes how the mobile app market is growing day by day. Mobile app stores have created the opportunity for the users to publicly provide feedback on mobile apps that they have installed or used. In this way, users are involved in the design and development of mobile apps, which was done by designers and developers before. Online user reviews are a useful source to know the user's perception about mobile apps and thus provide a way of co-value creation. This article is conducted to investigate the factors affecting the acceptability of mobile Apps. Main purpose of this article is to use online reviews for construction of a model instead of using existing acceptance theories. The model proposed in this research is based on the analysis of reviews and app information extracted from the Google Play Store. The ratings and number of installs are two key indicators of the popularity of an app. Other characteristics like price, category and size also influence the user's selection of an app. The findings showed the appropriateness of the proposed model and hypotheses for evaluating mobile apps acceptability and popularity. This article provides mobile app developers and marketers with an insight into the mobile app popularity and acceptability dynamics.