Detecting and Mitigating Points of Failure in Community Networks: A Graph-Based Approach

Maccari, Leonardo

doi:10.1109/tcss.2018.2890483

Cited by 8 publications

(7 citation statements)

References 40 publications

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“…CNs are generally treated as a not-for-profit initiative held in commons, often with no legal entity, which makes them easy to bootstrap, the exact opposite of the all-or-nothing model we mentioned. This design sparked a body of interdisciplinary research in ICT [5], social systems analysis [6], network economy [7], and many more. However, 2G-CNs design has three big drawbacks.…”

Section: A Motivations and Design Of A Modern Cnmentioning

confidence: 99%

Toward Smart Community Networks

Gemmi,

Cerdà-Alabern,

Navarro

et al. 2023

IEEE Network

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The advent of IEEE 802.11 in the late 1990s spurred the development of new network paradigms. In particular, new technology enthusiasts saw the potential of Wi-Fi to bring broadband Internet connections to under-provisioned areas, giving rise to networks deployed and maintained by their users. This paradigm led to non-profit decentralized structures that grow by the unplanned addition of heterogeneous network devices: community networks (CNs). There have been hundreds of CN deployments worldwide; some have disappeared, while others have blossomed into complex networks with thousands of nodes. The networking research community has been aware of CNs, and many works studied CNs in their various aspects: design (routing, scalability, security), deployment, measurements, services, etc.We argue that emerging technologies will give a new impetus to CNs by transforming them into smart CNs. This paper aims to lay out the technical features of future CNs and encourage the research community to tackle the stimulating research challenges they raise.

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Section: A Motivations and Design Of A Modern Cnmentioning

confidence: 99%

Toward Smart Community Networks

Gemmi,

Cerdà-Alabern,

Navarro

et al. 2023

IEEE Network

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“…[2], [3] in various fields, such as social networks [4], [5], biological networks [6], electric systems [7], economics [8], neural networks [9], etc. In practice, most of these systems are evolving with time, which can be modeled as dynamic networks with nodes or links appearing and disappearing over time [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction

Chen,

Xiong,

Zheng

et al. 2021

Preprint

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Dynamic link prediction (DLP) makes graph prediction based on historical information. Since most DLP methods are highly dependent on the training data to achieve satisfying prediction performance, the quality of the training data is crucial. Backdoor attacks induce the DLP methods to make wrong prediction by the malicious training data, i.e., generating a subgraph sequence as the trigger and embedding it to the training data. However, the vulnerability of DLP toward backdoor attacks has not been studied yet. To address the issue, we propose a novel backdoor attack framework on DLP, denoted as Dyn-Backdoor. Specifically, Dyn-Backdoor generates diverse initial-triggers by a generative adversarial network (GAN). Then partial links of the initial-triggers are selected to form a trigger set, according to the gradient information of the attack discriminator in the GAN, so as to reduce the size of triggers and improve the concealment of the attack. Experimental results show that Dyn-Backdoor launches successful backdoor attacks on the state-of-the-art DLP models with success rate more than 90%. Additionally, we conduct a possible defense against Dyn-Backdoor to testify its resistance in defensive settings, highlighting the needs of defenses for backdoor attacks on DLP.

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“…The empirical analysis of CN topologies that evolve guided by fully decentralized decisions, without any central coordination or intervention, has provided evidence of pathologies and emergent risks. These include high dependence (in terms of connectivity and routing functionality) on a single or a few nodes, which may turn to single points of failure for the CN (if, for some reason, their owners lose interest in the CN or move to another place), and large differences in routes and speeds connecting end users to the Internet [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The source code of this tool is published as an open source project and it is available on-line 5 . We highlight that the tool can be used in two main different ways.…”

Section: Introductionmentioning

confidence: 99%

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Towards scalable Community Networks topologies

et al. 2019

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Community Networks (CNs) are grassroots bottom-up initiatives that build local infrastructures, normally using Wi-Fi technology, to bring broadband networking in areas with inadequate offer of traditional infrastructures such as ADSL, FTTx or wide-band cellular (LTE, 5G). Albeit they normally operate as access networks to the Internet, CNs are ad-hoc networks that evolve based on local requirements and constraints, often including additional local services on top of Internet access. These networks grow in highly decentralized manner that radically deviates from the top-down network planning practiced in commercial mobile networks, depending, on the one hand, on the willingness of people to participate, and, on the other hand, on the feasibility of wireless links connecting the houses of potential participants with each other. In this paper, we present a novel methodology and its implementation into an automated tool, which enables the exercise of (light) centralized control to the dynamic and otherwise spontaneous CN growth process. The goal of the methodology is influencing the choices to connect a new node to the CN so that it can grow with more balance and to a larger size. Input to our methodology are open source resources about the physical terrain of the CN deployment area, such as Open Street Map and very detailed (less than 1 m resolution) LIDAR-based data about buildings layout and height, as well as technical descriptions and pricing data about off-the-shelf networking devices that are made available by manufacturers. Data related to demographics can be easily added to refine the environment description. With these data at hand, the tool can estimate the technical and economic feasibility of adding new nodes to the CN and actively assist new CN users in selecting proper equipment and CN node(s) to connect with to improve the CN scalability. We test our methodology in four different areas representing standard territorial characterization categories: urban, suburban, intermediate, and rural. In all four cases our tool shows that CNs scale to much larger size using the assisted, network-aware methodology when compared with de facto practices. Results also show that the CNs deployed with the assisted methodology are more balanced and have a lower per-node cost for the same per-node guaranteed bandwidth. Moreover, this is achieved with fewer devices per node, which means that the network is cheaper to build and easier to maintain.

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Detecting and Mitigating Points of Failure in Community Networks: A Graph-Based Approach

Cited by 8 publications

References 40 publications

Toward Smart Community Networks

Toward Smart Community Networks

Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction

Towards scalable Community Networks topologies

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