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Establishing Trust in Online Advertising With Signed Transactions
Get PDFProgrammatic advertising operates one of the most sophisticated and efficient service platforms on the Internet. However, the complexity of this ecosystem is a direct cause of one of the most important problems in online advertising, the lack of transparency. This lack of transparency enables subsequent problems such as advertising fraud, which causes billions of dollars in losses. In this paper we propose Ads.chain, a technological solution to the lack-of-transparency problem in programmatic advertising. Ads.chain extends the current effort of the Internet Advertising Bureau (IAB) in providing traceability in online advertising through the Ads.txt and Ads.cert solutions, addressing the limitations of these techniques. Ads.chain is (to the best of the authors’ knowledge) the first solution that provides end-to-end cryptographic traceability at the ad transaction level. It is a communication protocol that can be seamlessly embedded into ad-tags and the OpenRTB protocol, the de-facto standards for communications in online advertising, allowing an incremental adoption by the industry. We have implemented Ads.chain and made the code publicly available. We assess the performance of Ads.chain through a thorough analysis in a lab environment that emulates a real ad delivery process at real-life throughputs. The obtained results show that Ads.chain can be implemented with limited impact on the hardware resources and marginal delay increments at the publishers lower than 0.20 milliseconds per ad space on webpages and 2.6 milliseconds at the programmatic advertising platforms. These results confirm that Ads.chain’s impact on the user experience and the overall operation of the programmatic ad delivery process can be considered negligible.pu
vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs
Get PDFThe virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex dependencies between computing and radio resources make vRAN resource control particularly daunting. We present vrAIn, a dynamic resource orchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map contexts into resource control decisions. We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over a production RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods; (ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning, vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-free solution that does not need to assume any particular platform or context.pu
Apophanies or Epiphanies: How Crawlers Can Impact Our Understanding of the Web
Get PDFData generated by web crawlers has formed the basis for much of our current understanding of the Internet. However, not all crawlers are created equal and crawlers generally find themselves trading off between computational overhead, developer effort, data accuracy, and completeness. Therefore, the choice of crawler has a critical impact on the data generated and knowledge inferred from it. In this paper, we conduct a systematic study of the trade-offs presented by different crawlers and the impact that these can have on various types of measurement studies. We make the following contributions: First, we conduct a survey of all research published since 2015 in the premier security and Internet measurement venues to identify and verify the repeatability of crawling methodologies deployed for different problem domains and publication venues. Next, we conduct a qualitative evaluation of a subset of all crawling tools identified in our survey. This evaluation allows us to draw conclusions about the suitability of each tool for specific types of data gathering. Finally, we present a methodology and a measurement framework to empirically highlight the differences between crawlers and how the choice of crawler can impact our understanding of the web.TRUEpu
A Simple Model of MTC Flows Applied to Smart Factories
No full textIn this paper we develop a simple, yet accurate, performance model to understand if and how evolutions of standard cellular network protocols can be exploited to allow large numbers of machine type devices to access transmission resources with short latency, and we apply our model to the performance analysis of smart factory radio access networks. The model results shed light on the problems resulting from the application of evolved standard access procedures and help understand how many devices can be served per base station with specified latency targets. In addition, considering the simultaneous presence of different traffic classes, we investigate the effectiveness of prioritised access, exploiting access class barring techniques. Our model shows that, even with the sub-millisecond time slots foreseen in LTE Advanced Pro and 5G, a base station can accommodate at most few thousand devices to guarantee access latency below 100 ms with high transmission success probability. Lower access latency, of the order of 10 ms, can be achieved only with base stations serving an unrealistically small numbers of devices. This calls for a rethinking of wireless access strategies to avoid excessive latency in ultra-dense cell deployments within smart factory's infrastructures.pu
Microscope: Mobile Service Traffic Decomposition for Network Slicing as a Service
Get PDFThe growing diversification of mobile services imposes requirements on network performance that are ever more stringent and heterogeneous. Network slicing aligns mobile network operation to this context, by enabling operators to isolate and customize network resources on a per-service basis. A key input for provisioning resources to slices is real-time information about the traffic demands generated by individual services. Acquiring such knowledge is however challenging, as legacy approaches based on in-depth inspection of traffic streams have high computational costs, which inflate with the widening adoption of encryption over data and control traffic. In this paper, we present a new approach to service-level demand estimation for slicing, which hinges on decomposition, i.e., the inference of per-service demands from traffic aggregates. By operating on total traffic volumes only, our approach overcomes the complexity and limitations of legacy traffic classification techniques, and provides a suitable input to recent ‘Network Slice as a Service’ (NSaaS) models. We implement decomposition through Microscope, a novel framework that uses deep learning to infer individual service demands from complex spatiotemporal features hidden in traffic aggregates. Microscope (i) transforms traffic data collected in irregular radio access deployments in a format suitable for convolutional learning, and (ii) can accommodate a variety of neural network architectures, including original 3D Deformable Convolutional Neural Networks (3D-DefCNNs) that we explicitly design for decomposition. Experiments with measurement data collected in an operational network demonstrate that Microscope accurately estimates per-service traffic demands with relative errors below 1.2%. Further, tests in practical NSaaS management use cases show that resource allocations informed by decomposition yield affordable costs for the mobile network operator.TRUEpu
mm-FLEX: An Open Platform for Millimeter-Wave Mobile Full-Bandwidth Experimentation
Get PDFMillimeter-Wave (mm-wave) technology is increasingly being considered for mobile devices and use cases such as vehicular communication. This requires suitable experimentation platforms to support systems-oriented research to tackle the multitude of problems and challenges of mm-wave communications in such environments.
To this end, we introduce mm-FLEX, a flexible and modular open platform with real-time signal processing capabilities that supports a bandwidth of 2 GHz and is compatible with mm-wave standard requirements. mm-FLEX integrates an FPGA-based baseband processor with full-duplex capabilities together with mm-wave RF front-ends and phased antenna arrays that are fully configurable from the processor in real-time. To demonstrate the capabilities of mm-FLEX, we implement a scalable, ultra fast beam alignment mechanism for IEEE 802.11ad systems. It is based on compressive estimation of the signal’s angle-of-arrival by means of switching through multiple receive beam patterns on a nano-second timescale while receiving a packet preamble. Our implementation is
open source and is made publicly available to the research community.TRUEpu
Encrypted DNS –> Privacy? A Traffic Analysis Perspective
No full textVirtually every connection to an Internet service is preceded by a DNS lookup which is performed without any traffic-level protection, thus enabling manipulation, redirection, surveillance, and censorship. To address these issues, large organizations such as Google and Cloudflare are deploying recently standardized protocols that encrypt DNS traffic between end users and recursive resolvers such as DNS-over-TLS (DoT) and DNS-over-HTTPS (DoH). In this paper, we examine whether encrypting DNS traffic can protect users from traffic analysis-based monitoring and censoring. We propose a novel feature set to perform the attacks, as those used to attack HTTPS or Tor traffic are not suitable for DNS’ characteristics. We show that traffic analysis enables the identification of domains with high accuracy in closed and open world settings, using 124 times less data than attacks on HTTPS flows. We find that factors such as location, resolver, platform, or client do mitigate the attacks performance but they are far from completely stopping them. Our results indicate that DNS-based censorship is still possible on encrypted DNS traffic. In fact, we demonstrate that the standardized padding schemes are not effective. Yet, Tor — which does not effectively mitigate traffic analysis attacks on web traffic— is a good defense against DoH traffic analysis.TRUEpu
On the Use of Small Solar Panels and Small Batteries to Reduce the RAN Carbon Footprint
Get PDFhttps://doi.org/10.1109/MedComNet49392.2020.9191516The limited power requirements of new generations of base stations make the use of renewable energy sources, solar in particular, extremely attractive for mobile network operators. Exploiting solar energy implies a reduction of the network operation cost as well as of the carbon footprint of radio access networks. However, previous research works indicate that the area of the solar panels that are necessary to power a standard macro base station (BS) is large, making the solar panel deployment problematic, especially within urban areas. In this paper we use a modeling approach based on Markov reward processes to investigate the possibility of combining a connection to the power grid with small area solar panels and small batteries to run a macro base station. By so doing, it is possible to exploit a significant fraction of renewable energy to run a radio access network, while also reducing the cost incurred by the network operator to power its base stations. We assume that energy is drawn from the power grid only when needed to keep the BS operational, or during the night, which corresponds to the period with lowest electricity price. The proposed energy management policies have advantages in terms of both cost and carbon footprint. Our results show that solar panels of the order of 1-2 kW peak, i.e., with a surface of about 5-10 m 2 , combined with limited capacity energy storage (of the order of 1-5 kWh, corresponding to about 1-2 car batteries) and a smart energy management policy, can lead to an effective exploitation of renewable energy.TRUEpu
LSTM-based GNSS Spoofing Detection Using Low-cost Spectrum Sensors
Get PDFGNSS/GPS is a positioning system widely used nowadays in our lives for real-time localization in Earth. This technology is highly vulnerable to spoofing/jamming attacks caused by malicious intruders. In the recent years, commodity and low-cost radio-frequency hardware have been used to interfere with the legitimate GPS signal. Existing spoofing detection solutions use costly receivers and computationally expensive algorithms which limit the large-scale deployment. In this work we propose a GNSS spoofing detection system that can run on spectrum sensors with Software-Defined Radio (SDR) capabilities and cost in the order of 20 euros. Our approach exploits the predictability of the Doppler characteristics of the received GPS signals to determine the presence of anomalies or malicious attackers. We propose an artificial recurrent neural network (RNN) based on Long short-term memory (LSTM) for anomaly detection. We use data received by low-cost SDR receivers that are processed locally by low-cost embedded machines such as Nvidia Jetson Nano to provide inference capabilities. We show that our solution predicts very accurately the Doppler shift of GNSS signals and can determine the presence of a spoofing transmitter.TRUEpu
Beam Search Strategy for MillimeterWave Networks with Out-of-Band Input Data
Get PDFBeam training in dynamic millimeter-wave (mm-wave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional antennas under mobility. Exploiting the fact that mobile devices will typically integrate multiple chipsets, we study a set of non-mmwave input data that can be leveraged jointly to provide faster beam search and better data rate. We leverage these findings to introduce SLASH, an algorithm that adaptively narrows the sector search space and accelerates link establishment, link maintenance and handover between mm-wave devices. We evaluate SLASH both with simulations and experiments in a 60-GHz testbed. SLASH can increase the data rate by more than 64% for link establishment and 67% for link maintenance with respect to prior work.TRUEpu