5 research outputs found
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
Dynamic Interference Adaptation for Wireless Mesh Networks
ABSTRACT Most wireless meshes will have to operate within the crowded unlicensed spectrum that is also shared by numerous uncoordinated 802.11 hotspot
Climate-smart agriculture global research agenda: Scientific basis for action
Background: Climate-smart agriculture (CSA) addresses the challenge of meeting the growing demand for food, fibre and fuel, despite the changing climate and fewer opportunities for agricultural expansion on additional lands. CSA focuses on contributing to economic development, poverty reduction and food security; maintaining and enhancing the productivity and resilience of natural and agricultural ecosystem functions, thus building natural capital; and reducing trade-offs involved in meeting these goals. Current gaps in knowledge, work within CSA, and agendas for interdisciplinary research and science-based actions identified at the 2013 Global Science Conference on Climate-Smart Agriculture (Davis, CA, USA) are described here within three themes: (1) farm and food systems, (2) landscape and regional issues and (3) institutional and policy aspects. The first two themes comprise crop physiology and genetics, mitigation and adaptation for livestock and agriculture, barriers to adoption of CSA practices, climate risk management and energy and biofuels (theme 1); and modelling adaptation and uncertainty, achieving multifunctionality, food and fishery systems, forest biodiversity and ecosystem services, rural migration from climate change and metrics (theme 2). Theme 3 comprises designing research that bridges disciplines, integrating stakeholder input to directly link science, action and governance. Outcomes: In addition to interdisciplinary research among these themes, imperatives include developing (1) models that include adaptation and transformation at either the farm or landscape level; (2) capacity approaches to examine multifunctional solutions for agronomic, ecological and socioeconomic challenges; (3) scenarios that are validated by direct evidence and metrics to support behaviours that foster resilience and natural capital; (4) reductions in the risk that can present formidable barriers for farmers during adoption of new technology and practices; and (5) an understanding of how climate affects the rural labour force, land tenure and cultural integrity, and thus the stability of food production. Effective work in CSA will involve stakeholders, address governance issues, examine uncertainties, incorporate social benefits with technological change, and establish climate finance within a green development framework. Here, the socioecological approach is intended to reduce development controversies associated with CSA and to identify technologies, policies and approaches leading to sustainable food production and consumption patterns in a changing climate