Paying for Likes? Understanding Facebook like fraud using honeypots

Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods. This paper presents a comparative measurement study of page likes garnered via Facebook ads and by a few like farms. We deploy a set of honeypot pages, promote them using both methods, and analyze garnered likes based on likers' demographic, temporal, and social characteristics. We highlight a few interesting findings, including that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior

TCP throughput guarantee in the DiffServ Assured Forwarding service: what about the results?

Since the proposition of Quality of Service architectures by the IETF, the interaction between TCP and the QoS services has been intensively studied. This paper proposes to look forward to the results obtained in terms of TCP throughput guarantee in the DiffServ Assured Forwarding (DiffServ/AF) service and to present an overview of the different proposals to solve the problem. It has been demonstrated that the standardized IETF DiffServ conditioners such as the token bucket color marker and the time sliding window color maker were not good TCP traffic descriptors. Starting with this point, several propositions have been made and most of them presents new marking schemes in order to replace or improve the traditional token bucket color marker. The main problem is that TCP congestion control is not designed to work with the AF service. Indeed, both mechanisms are antagonists. TCP has the property to share in a fair manner the bottleneck bandwidth between flows while DiffServ network provides a level of service controllable and predictable. In this paper, we build a classification of all the propositions made during these last years and compare them. As a result, we will see that these conditioning schemes can be separated in three sets of action level and that the conditioning at the network edge level is the most accepted one. We conclude that the problem is still unsolved and that TCP, conditioned or not conditioned, remains inappropriate to the DiffServ/AF service

FORGE enabling FIRE facilities for the eLearning community

International audienceMany engineering students at third-level institutions across the world will not have the advantage of using real-world experimentation equipment, as the infrastructure and resources required for this activity are too expensive. This paper explains how the FORGE (Forging Online Education through FIRE) FP7 project transforms Future Internet Research and Experimentation (FIRE) testbed facilities into educational resources for the eLearning community. This is achieved by providing a framework for remote experimentation that supports easy access and control to testbed infrastructure for students and educators. Moreover, we identify a list of recommendations to support development of eLearning courses that access these facilities and highlight some of the challenges encountered by FORGE

Deep Content: Unveiling video streaming content from encrypted WiFi Traffic

© 2018 IEEE. The proliferation of smart devices has led to an exponential growth in digital media consumption, especially mobile video for content marketing. The vast majority of the associated Internet traffic is now end-to-end encrypted, and while encryption provides better user privacy and security, it has made network surveillance an impossible task. The result is an unchecked environment for exploiters and attackers to distribute content such as fake, radical and propaganda videos. Recent advances in machine learning techniques have shown great promise in characterising encrypted traffic captured at the end points. However, video fingerprinting from passively listening to encrypted traffic, especially wireless traffic, has been reported as a challenging task due to the difficulty in distinguishing retransmissions and multiple flows on the same link. We show the potential of fingerprinting videos by passively sniffing WiFi frames in air, even without connecting to the WiFi network. We have developed Multi-Layer Perceptron (MLP) and Recurrent Neural Networks (RNNs) that are able to identify streamed YouTube videos from a closed set, by sniffing WiFi traffic encrypted at both Media Access Control (MAC) and Network layers. We compare these models to the state-of-the-art wired traffic classifier based on Convolutional Neural Networks (CNNs), and show that our models obtain similar results while requiring significantly less computational power and time (approximately a threefold reduction)

Fast privacy-preserving network function outsourcing

In this paper, we present the design and implementation of SplitBox, a system for privacy-preserving processing of network functions outsourced to cloud middleboxes—i.e., without revealing the policies governing these functions. SplitBox is built to provide privacy for a generic network function that abstracts the functionality of a variety of network functions and associated policies, including firewalls, virtual LANs, network address translators (NATs), deep packet inspection, and load balancers. We present a scalable design aiming to provide high throughput and low latency, by distributing functionalities to a few virtual machines (VMs), while providing provably secure guarantees. We implement SplitBox inside FastClick, an extension of the Click modular router, using Intel's DPDK to handle packet I/O. We evaluate our prototype experimentally to find its bottlenecks and stress-test its different components, vis-à-vis two widely used network functions, i.e., firewall and VLAN tagging. Our evaluation shows that, on commodity hardware, SplitBox can process packets close to line rate (i.e., 8.9Gbps) with up to 50 traversed policies

Effectiveness criteria for customised agricultural life cycle assessment tools

Greater use of life cycle assessment (LCA) by agents of change will be needed to inform environmental improvements in agriculture, but the complexity of LCA can be a barrier. More accessible LCA tools customised for agriculture are emerging, but their effectiveness has not been considered. The aim of the work was to understand how tool features influence effectiveness and to propose criteria for effectiveness, for informing the design and evaluation of tools. We define 'customised' tools as those that focus on the life cycle phases and aspects of most relevance for the particular sector (in this case agriculture), and that parameterise practice variables to enable evaluation of practice alternatives. A theoretical framework for the role of tools in agricultural practice change was first used to define the desired objectives of LCA tools: i) to engage agricultural agents of change by catering to their needs, being accessible and being easy to use, ii) to generate information that users can interpret for informing environmental improvements, and iii) to generate information that can align with the wider decision making context. A desktop review of 14 LCA customised agriculture tools identified the features that influence these objectives: tool purpose, mode of access, ease of use, results presentation, degree of practice parameterisation, capacity for regionalised analysis, system scope, impact categories assessed, and alignment with other assessment frameworks. From this, a set of effectiveness criteria for customised LCA tools was developed. A few criteria from amongst this set will be challenges for future tool development: the balance between analysis capacity and ease of use, enabling regionalised analysis, and the presentation of results in a way that aids interpretation for informing environmental improvements. (C) 2017 Elsevier Ltd. All rights reserved

Developpement de la vigne et maturation du raisin dans differents terroirs viticoles de la moyenne vallee de la Loire

National audienc

18th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, WoWMoM 2017 - Conference

© 2017 IEEE. Dynamic Adaptive Streaming over HTTP (DASH) is one of the most popular ways to stream videos at present. In this work, we propose a DASH player energy-aware plugin (eDASH) for mobile devices which help reduce the battery consumption of the device. The eDASH player utilises a novel bitrate and video brightness adaptation algorithm to determine the next chunk to download. This algorithm utilises an energy-aware QoE model which factors in power consumption of the device in conjunction with existing bitrate adaptation logic to determine the next chunk. We also propose a new DASH architecture which could be easily integrated with the existing one. Macro-benchmarking of energy consumption of a mobile device while streaming and playing back video is conducted to obtain energy profiles of various video qualities. This energy data is then used along with real world network traces to drive simulations to evaluate energy savings that could be achieved using eDASH. We observe that up to 45% energy savings could be achieved with minimal reduction is QoE. We also find that up to 80% data transfer savings could also be achieved with an eDASH client

Relationship between sensory analysis, penetrometry and visible-NIR spectroscopy of apples belonging to different cultivars

National audienc