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Towards an extensible privacy analysis framework for Smart Homes
Get PDFThe IoT ecosystem is an intricate and complex network of stake- holders that includes platforms, developers, ad networks and cloud providers. However, the ability of smart home platforms and devices to interact and exchange data, together with the data-driven busi- ness models adopted by most IoT stakeholders open the ground for unknown and unexpected privacy risks. Existing black-box testing approaches to audit IoT platforms cannot identify data dissemina- tion through side- and covert-channels, and for this reason they are not well suited for rich execution environments where a wide range of devices and applications can co-operate using multiple network protocols and interfaces. This poster proposes ImposTer, a cost-effective and extensible privacy framework for exhaustively testing the IoT ecosystem. Our framework is able to capture, model and emulate horizontal interactions that occur across the different devices in a consumer household.ODIOTrustAwareTRUEpu
Deep Learning-based Multivariate Time Series Classification for Indoor/Outdoor Detection
Get PDFRecently, the topic of indoor outdoor detection (IOD) has seen its popularity increase, as IOD models can be leveraged to augment the performance of numerous Internet of Things and other applications. IOD aims at distinguishing in an efficient manner whether a user resides in an indoor or an outdoor environment, by inspecting the cellular phone sensor recordings. Legacy IOD models attempt to determine a user’s environment by comparing the sensor measurements to some threshold values. However, as we also observe in our experiments, such models exhibit limited scalability, and their accuracy can be poor. Machine learning (ML)-based IOD models aim at removing this limitation, by utilizing a large volume of measurements to train ML algorithms to classify a user’s environment. Yet, in most of the existing research, the temporal dimension of the problem is disregarded. In this paper, we propose treating IOD as a multivariate time series classification (TSC) problem, and we explore the performance of various deep learning (DL) models. We demonstrate that a multivariate TSC approach can be used to monitor a user’s environment, and predict changes in its state, with greater accuracy compared to conventional approaches that ignore the feature variation over time. Additionally, we introduce a new DL model for multivariate TSC, exploiting the concept of self-attention and atrous spatial pyramid pooling. The proposed DL multivariate TSC framework exploits only low power consumption sensors to infer a user’s environment, and it outperforms state-of-the-art models, yielding a higher accuracy combined with a smaller computational cost.European CommissionTRUEpu
A Unified Graph-Based Approach to Disinformation Detection using Contextual and Semantic Relations
Get PDFAs recent events have demonstrated, disinformation spread through social networks can have dire political, economic and social consequences. Detecting disinformation must inevitably rely on the structure of the network, on users particularities and on event occurrence patterns. We present a graph data structure, which we denote as a meta-graph, that combines underlying users' relational event information, as well as semantic and topical modeling. We detail the construction of an example meta-graph using Twitter data covering the 2016 US election campaign and then compare the detection of disinformation at cascade level, using well-known graph neural network algorithms, to the same algorithms applied on the meta-graph nodes. The comparison shows a consistent 3%-4% improvement in accuracy when using the meta-graph, over all considered algorithms, compared to basic cascade classification, and a further 1% increase when topic modeling and sentiment analysis are considered. We carry out the same experiment on two other datasets, HealthRelease and HealthStory, part of the FakeHealth dataset repository, with consistent results. Finally, we discuss further advantages of our approach, such as the ability to augment the graph structure using external data sources, the ease with which multiple meta-graphs can be combined as well as a comparison of our method to other graph-based disinformation detection frameworks.IMDEA NetworksTRUEpu
A Comparative Analysis of Certificate Pinning in Android & iOS
Get PDFTLS certificate pinning is a security mechanism used by applications (apps) to protect their network traffic against malicious certificate authorities (CAs), in-path monitoring, and other TLS tampering. Pinning can provide enhanced security to defend against malicious third-party access to sensitive data in transit (e.g.,to protect sensitive banking and health care information), but can also hide an app’s personal data collection from users and auditors. Prior studies found pinning was rarely used in the Android ecosystem; however, little is known about recent pinning usage on iOS and across mobile platforms.
In this paper, we thoroughly investigate the use of certificate pinning on Android and iOS. We collect 5,079 unique apps from the two official app stores: 575 common apps, 1,000 popular apps each, and 1,000 randomly selected apps each. We develop novel, cross-platform, static and dynamic analysis techniques to detect certificate pinning, not only based on static configurations, but also its run-time use.
We find certificate pinning as much as 4 times more widely adopted than reported in prior studies. More specifically, we find that at least 0.9% to 8% of Android apps and 2.5% to 11% of iOS apps use certificate pinning (depending on the above groups of apps). We then investigate which categories of apps most frequently use pinning (apps in the “finance” category), which destinations are typically pinned (first-party destinations vs those used by third-party libraries), which certificates are pinned and how they are pinned (CA vs leaf certificates), and the connection security for pinned connections vs unpinned ones (e.g., the use of weak ciphers or improper certificate validation). Last, we investigate how many pinned connections are amenable to binary instrumentation for revealing the contents of their connections, and for those that are, we analyze the data sent in pinned connections to understand what is protected by pinning.TRUEpu
Setchain: Improving Blockchain Scalability with Byzantine Distributed Sets and Barriers
Get PDFBlockchain technologies are facing a scalability challenge, which must be overcome to guarantee a wider adoption of the technology. This scalability issue is due to the use of consensus algorithms to guarantee the total order of the chain of blocks (and of the transactions within each block). However, total order is often overkilling, since important advanced applications of smart-contracts do not require a total order among all operations. A much higher scalability can potentially be achieved if a more relaxed order (instead of a total order) can be exploited.
In this paper, we propose a distributed concurrent data type, called Setchain, which improves scalability signifi- cantly. A Setchain implements a grow-only set object whose elements are not ordered, unlike conventional blockchain operations. When convenient, the Setchain allows forcing a synchronization barrier that assigns permanently an epoch number to a subset of the latest elements added. Therefore, two operations in the same epoch are not ordered, while two operations in different epochs are ordered by their respective epoch number. We present different Byzantine- tolerant implementations of Setchain, prove their correctness and report on an empirical evaluation of a prototype implementation.
Our results show that Setchain is orders of magnitude faster than consensus-based ledgers, since it implements grow-only sets with epoch synchronization instead of total order. Moreover, since the Setchain barriers can be synchronized with the underlying blockchain, Setchain objects can be used as a sidechain to implement many smart contract solutions with much faster operations than on basic blockchains.Nomadic LabsTezos FoundationTRUEpu
V-Edge: Virtual Edge Computing as an Enabler for Novel Microservices and Cooperative Computing
Get PDFAs we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: Instead of sending all data and tasks from an end user’s device to the cloud, possibly covering thousands of kilometers and introducing delays lower-bounded by propagation speed, edge servers deployed in close proximity to the user, e.g., at some base station, serve as proxy for the cloud. This is particularly interesting for upcoming machine learning (ML)-based intelligent services, which require substantial computational and networking performance for continuous model training. Yet this promising idea is hampered by the limited number of such edge servers. In this position paper, we discuss a way forward, namely the virtual edge computing (V-Edge) concept. V-Edge helps bridging the gap between cloud, edge, and fog by virtualizing all available resources including the end users’ devices and making these resources widely available. Thus, V-Edge acts as an enabler for novel microservices as well as cooperative computing solutions in next-generation networks. We introduce the general V-Edge architecture and we characterize some of the key research challenges to overcome in order to enable wide-spread and intelligent edge services.TRUEpu
Stabilization, Safety, and Security of Distributed Systems - 24th International Symposium, SSS 2022, Clermont-Ferrand, France, November 15-17, 2022, Proceedings
No full textTRUEpu
LossLeaP: Learning to Predict for Intent-Based Networking
Get PDFIntent-Based Networking mandates that high-levelhuman-understandable intents are automatically interpreted andimplemented by network management entities. As a key partin this process, it is required that network orchestrators acti-vate the correct automated decision model to meet the intentobjective. In anticipatory networking tasks, this requirementmaps to identifying and deploying a tailored prediction modelthat can produce a forecast aligned with the specific –andtypically complex– network management goal expressed by theoriginal intent. Current forecasting models for network demandsor network management optimize generic, non-flexible, andmanually designed objectives, hence do not fulfil the needsof anticipatory Intent-Based Networking. To close this gap,we proposeLossLeaP, a novel forecasting model that canautonomously learn the relationship between the prediction andthe target management objective, steering the former to minimizethe latter. To this end,LossLeaPadopts an original deeplearning architecture that advances current efforts in automatedmachine learning, towards a spontaneous design of loss func-tions for regression tasks. Extensive experiments in controlledenvironments and in practical application case studies prove thatLossLeaPoutperforms a wide range of benchmarks, includingstate-of-the-art solutions for network capacity forecasting.TRUEpu
SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems
Get PDFA well established method to detect and classify human movements
using Millimeter-Wave ( mmWave) devices is the time-frequency
analysis of the small-scale Doppler effect (termed micro-Doppler)
of the different body parts, which requires a regularly spaced and
dense sampling of the Channel Impulse Response ( CIR). This is cur-
rently done in the literature either using special-purpose radar sen-
sors, or interrupting communications to transmit dedicated sensing
waveforms, entailing high overhead and channel utilization. In this
work we present SPARCS, an integrated human sensing and commu-
nication solution for mmWave systems. SPARCS is the first method
that reconstructs high quality signatures of human movement from
irregular and sparse CIR samples, such as the ones obtained during
communication traffic patterns. To accomplish this, we formulate
the micro-Doppler extraction as a sparse recovery problem, which
is critical to enable a smooth integration between communication
and sensing. Moreover, if needed, our system can seamlessly inject
short CIR estimation fields into the channel whenever communica-
tion traffic is absent or insufficient for the micro-Doppler extraction.
SPARCS effectively leverages the intrinsic sparsity of the mmWave
channel, thus drastically reducing the sensing overhead with re-
spect to available approaches. We implemented SPARCS on an
IEEE 802.11ay Software Defined Radio (SDR) platform working in
the 60 GHz band, collecting standard-compliant CIR traces match-
ing the traffic patterns of real WiFi access points. Our results show
that the micro-Doppler signatures obtained by SPARCS enable a
typical downstream application such as human activity recognition
with more than 7 times lower overhead with respect to existing
methods, while achieving better recognition performance.TRUEpu
Using indirect reporting surveys to monitor the COVID-19 pandemic
No full textIn this talk, we present the Network Scale-up Method of indirect reporting, and how it can be used to monitor the incidence of the COVID-19 pandemic around the world.FALSEpu