FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure

Towards Identifying Paid Open Source Developers - A Case Study with Mozilla Developers

Open source development contains contributions from both hired and volunteer software developers. Identification of this status is important when we consider the transferability of research results to the closed source software industry, as they include no volunteer developers. While many studies have taken the employment status of developers into account, this information is often gathered manually due to the lack of accurate automatic methods. In this paper, we present an initial step towards predicting paid and unpaid open source development using machine learning and compare our results with automatic techniques used in prior work. By relying on code source repository meta-data from Mozilla, and manually collected employment status, we built a dataset of the most active developers, both volunteer and hired by Mozilla. We define a set of metrics based on developers' usual commit time pattern and use different classification methods (logistic regression, classification tree, and random forest). The results show that our proposed method identify paid and unpaid commits with an AUC of 0.75 using random forest, which is higher than the AUC of 0.64 obtained with the best of the previously used automatic methods.Comment: International Conference on Mining Software Repositories (MSR) 201

Signposts: Resource for staff developers

This guide is for staff developers who work with new tertiary teachers, and provides guidelines on how to use 'Signposts: A professional development resource for new teaching staff in the tertiary sector'. It is the result of a project funded by the Ako Aotearoa Northern Hub

Raising the Datagram API to Support Transport Protocol Evolution

Some application developers can wield huge resources to build new transport protocols, for these developers the present UDP Socket API is perfectly fine. They have access to large test beds and sophisticated tools. Many developers do not have these resources. This paper presents a new high-level Datagram API that is for everyone else, this has an advantage of offering a clear evolutionary path to support new requirements. This new API is needed to move forward the base of the system, allowing developers with limited resources to evolve their applications while accessing new network services

Variation in the Use of Subregional Integration Courts between Business and Human Rights Actors: The Case of the East African Court of Justice

Residential energy visualization has increased in popularity during the past years, due to both legislation and an increased focus on the environmental impact of buildings. Meanwhile, the European energy efficiency directive has raised a debate on legislation on individual metering and charging (IMC), in which many negative voices among property owners and developers are being raised. The controversies bring interesting aspects to the analysis of energy visualization and its prerequisite IMC. This thesis will analyze the possibilities and barriers to implement residential energy visualization in new buildings in Stockholm, and the focus will be on local developers' perspective. The purpose of the thesis is to establish Stockholm developers' willingness to pay (WTP) for an IMC and energy visualization solution. The thesis defines perceived utility as the driving force for WTP, and accordingly the developer WTP is analyzed by evaluation of the developers' perceived utility of different technical aspects of an energy visualization solution. The solution has been modularized into three modules; IMC of hot water, IMC of heating and residential visualization. The hypothesis is that utility of the solution modules is perceived differently depending on developer ownership and developer business model; if the developer builds for property management or to sell. The empirical data has been collected through twelve in-depth interviews with developers in Stockholm. The developers were of different size, ownership and with different business models. When looking at the developers from an overall perspective, the analysis shows that there is some willingness to pay for IMC of hot water but none for IMC of heating. It can also be seen that residential visualization is something that the developers have some interest in although the overall WTP is considered low. Although environmental and fairness aspects are often mentioned by the developers in the context of IMC and energy visualization, operational and financial utility seem to be more influential in driving willingness to pay and as these utilities are not perceived, the overall WTP is low or non-existent for IMC and energy visualization. The hypothesis that developers would perceive utility differently depending on ownership or business model, if they build for property management of for sales, could not be proven. There are possibly tendencies for such differences but in this study such patterns were not clear enough to state the hypothesis as true. Additional to the WTP and developer groups, insights and takeaways are presented. The insights and takeaways are based on opportunities and risks that developers perceive with IMC and visualization, as well as requirements they have on the systems