arXiv subjects

David Starobinski

Publications and source records attributed to David Starobinski.

At least 19 recordsLinked to original sources

arXiv2026

Scaling the Lightning Network with Practical Set Reconciliation

The Lightning Network (LN) utilizes gossip to share network topology, channel announcements and updates, and node announcements among its local constituents. Yet, our measurements show that this flooding-based gossip reconciliation is fundamentally inefficient. We propose, instead, to use set reconciliation protocols for sharing this information, and we systematically evaluate existing approaches under realistic network conditions. We further propose ADAPTIVEIBLT, a novel adaptive IBLT (Invertible Bloom Lookup Table) protocol with a partial-decoding enhancement. By simulating reconciliation in Core-Lightning and evaluating real gossip snapshots, we demonstrate the practical benefits of reconciliation in scaling gossip reconciliation from hours down to a few minutes.

cs.NI↗

arXiv2026

FixV2W: Correcting Invalid CVE-CWE Mappings with Knowledge Graph Embeddings

Accurate mapping between Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) entries is critical for effective vulnerability management and risk assessment. However, public databases, such as the National Vulnerability Database (NVD), suffer from inconsistent and incomplete CVE to CWE mappings, complicating automated analysis and remediation. We introduce FixV2W, a lightweight approach that leverages knowledge graph embeddings and longitudinal trends to improve mapping accuracy of the NVD. FixV2W systematically analyzes historical remapping patterns and leverages hierarchical relationships within NVD and CWE data to predict more precise CWE mappings for vulnerabilities linked to Prohibited or Discouraged categories. We run extensive experimental evaluation of FixV2W, based on test data set collected between August 2021 and December 2024. Considering the Top 10 ranked predictions, the results show that FixV2W predicts the correct CWE mappings for 69% of exploited vulnerabilities that had invalid CWEs before they were exploited. We also show that FixV2W significantly improves the performance of ML models relying on NVD data. For instance, for a model geared at uncovering unknown CVE-CWE mappings, FixV2W improves the Mean Reciprocal Rank (MRR) from 0.174 to 0.608. These results show that FixV2W is a promising approach to identify and thwart emerging threats.

cs.CR↗

arXiv2026

Physics-grounded Mechanism Design for Spectrum Sharing between Passive and Active Users

We propose a physics-grounded mechanism design for dynamic spectrum sharing that bridges the gap between radiometric retrieval constraints and economic incentives. We formulate the active and passive users coexistence problem as a Vickrey-Clarke-Groves (VCG) auctions mechanism, where the radiometer dynamically procures ``quiet'' time-frequency tiles from active users based on the marginal reduction in retrieval error variance. This approach ensures allocative efficiency and dominant-strategy incentive compatibility (DSIC). To overcome the computational intractability of exact VCG on large grids, we derive an approximation algorithm by using the monotone submodularity induced by the radiometer equation. AMSR-2-based simulations show that the approach avoids high-cost tiles by aggregating low-cost spectrum across time and frequency. In an interference-trap case study, the proposed framework reduces procurement costs by about 60% over a fixed-band baseline while satisfying accuracy targets.

math.AP↗

arXiv2025

VDGraph: A Graph-Theoretic Approach to Unlock Insights from SBOM and SCA Data

The high complexity of modern software supply chains necessitates tools such as Software Bill of Materials (SBOMs) to manage component dependencies, and Software Composition Analysis (SCA) tools to identify vulnerabilities. While there exists limited integration between SBOMs and SCA tools, a unified view of complex dependency-vulnerability relationships remains elusive. In this paper, we introduce VDGraph, a novel knowledge graph-based methodology for integrating vulnerability and dependency data into a holistic view. VDGraph consolidates SBOM and SCA outputs into a graph representation of software projects' dependencies and vulnerabilities. We provide a formal description and analysis of the theoretical properties of VDGraph and present solutions to manage possible conflicts between the SBOM and SCA data. We further introduce and evaluate a practical, proof-of-concept implementation of VDGraph using two popular SBOM and SCA tools, namely CycloneDX Maven plugin and Google's OSV-Scanner. We apply VDGraph on 21 popular Java projects. Through the formulation of appropriate queries on the graphs, we uncover the existence of concentrated risk points (i.e., vulnerable components of high severity reachable through numerous dependency paths). We further show that vulnerabilities predominantly emerge at a depth of three dependency levels or higher, indicating that direct or secondary dependencies exhibit lower vulnerability density and tend to be more secure. Thus, VDGraph contributes a graph-theoretic methodology that improves visibility into how vulnerabilities propagate through complex, transitive dependencies. Moreover, our implementation, which combines open SBOM and SCA standards with Neo4j, lays a foundation for scalable and automated analysis across real-world projects.

cs.SE↗

arXiv2025

Approximation-First Timeseries Monitoring Query At Scale

Timeseries monitoring systems such as Prometheus play a crucial role in gaining observability of the underlying system components. These systems collect timeseries metrics from various system components and perform monitoring queries over periodic window-based aggregations (i.e., rule queries). However, despite wide adoption, the operational costs and query latency of rule queries remain high. In this paper, we identify major bottlenecks associated with repeated data scans and query computations concerning window overlaps in rule queries, and present PromSketch, an approximation-first query framework as intermediate caches for monitoring systems. It enables low operational costs and query latency, by combining approximate window-based query frameworks and sketch-based precomputation. PromSketch is implemented as a standalone module that can be integrated into Prometheus and VictoriaMetrics, covering 70% of Prometheus' aggregation over time queries. Our evaluation shows that PromSketch achieves up to a two orders of magnitude reduction in query latency over Prometheus and VictoriaMetrics, while lowering operational dollar costs of query processing by two orders of magnitude compared to Prometheus and by at least 4x compared to VictoriaMetrics with at most 5% average errors across statistics. The source code has been made available at https://github.com/Froot-NetSys/promsketch.

cs.DB↗

arXiv2023

Uncovering CWE-CVE-CPE Relations with Threat Knowledge Graphs

Security assessment relies on public information about products, vulnerabilities, and weaknesses. So far, databases in these categories have rarely been analyzed in combination. Yet, doing so could help predict unreported vulnerabilities and identify common threat patterns. In this paper, we propose a methodology for producing and optimizing a knowledge graph that aggregates knowledge from common threat databases (CVE, CWE, and CPE). We apply the threat knowledge graph to predict associations between threat databases, specifically between products, vulnerabilities, and weaknesses. We evaluate the prediction performance both in closed world with associations from the knowledge graph, and in open world with associations revealed afterward. Using rank-based metrics (i.e., Mean Rank, Mean Reciprocal Rank, and Hits@N scores), we demonstrate the ability of the threat knowledge graph to uncover many associations that are currently unknown but will be revealed in the future, which remains useful over different time periods. We propose approaches to optimize the knowledge graph, and show that they indeed help in further uncovering associations.

cs.CR↗

arXiv2023

Enabling Cost-Benefit Analysis of Data Sync Protocols

The problem of data synchronization arises in networked applications that require some measure of consistency. Indeed data synchronization approaches have demonstrated a significant potential for improving performance in various applications ranging from distributed ledgers to fog-enabled storage offloading for IoT. Although several protocols for data sets synchronization have been proposed over the years, there is currently no widespread utility implementing them, unlike the popular Rsync utility available for file synchronization. To that end, we describe a new middleware called GenSync that abstracts the subtleties of the state-of-the-art data synchronization protocols, allows users to choose protocols based on a comparative evaluation under realistic system conditions, and seamlessly integrate protocols in existing applications through a public API. We showcase GenSync through a case study, in which we integrate it into one of the world's largest wireless emulators and compare the performance of its included protocols.

cs.DC↗

arXiv2023

SREP: Out-Of-Band Sync of Transaction Pools for Large-Scale Blockchains

Synchronization of transaction pools (mempools) has shown potential for improving the performance and block propagation delay of state-of-the-art blockchains. Indeed, various heuristics have been proposed in the literature to this end, all of which incorporate exchanges of unconfirmed transactions into their block propagation protocol. In this work, we take a different approach, maintaining transaction synchronization outside (and independently) of the block propagation channel. In the process, we formalize the synchronization problem within a graph theoretic framework and introduce a novel algorithm (SREP - Set Reconciliation-Enhanced Propagation) with quantifiable guarantees. We analyze the algorithm's performance for various realistic network topologies, and show that it converges on any connected graph in a number of steps that is bounded by the diameter of the graph. We confirm our analytical findings through extensive simulations that include comparison with MempoolSync, a recent approach from the literature. Our simulations show that SREP incurs reasonable overall bandwidth overhead and, unlike MempoolSync, scales gracefully with the size of the network.

cs.DC↗

arXiv2023

GRAND-EDGE: A Universal, Jamming-resilient Algorithm with Error-and-Erasure Decoding

Random jammers that overpower transmitted signals are a practical concern for many wireless communication protocols. As such, wireless receivers must be able to cope with standard channel noise and jamming (intentional or unintentional). To address this challenge, we propose a novel method to augment the resilience of the recent family of universal error-correcting GRAND algorithms. This method, called Erasure Decoding by Gaussian Elimination (EDGE), impacts the syndrome check block and is applicable to any variant of GRAND. We show that the proposed EDGE method naturally reverts to the original syndrome check function in the absence of erasures caused by jamming. We demonstrate this by implementing and evaluating GRAND-EDGE and ORBGRAND-EDGE. Simulation results, using a Random Linear Code (RLC) with a code rate of $105/128$, show that the EDGE variants lower both the Block Error Rate (BLER) and the computational complexity by up to five order of magnitude compared to the original GRAND and ORBGRAND algorithms. We further compare ORBGRAND-EDGE to Ordered Statistics Decoding (OSD), and demonstrate an improvement of up to three orders of magnitude in the BLER.

cs.IT↗

arXiv2022

A General Security Approach for Soft-information Decoding against Smart Bursty Jammers

Malicious attacks such as jamming can cause significant disruption or complete denial of service (DoS) to wireless communication protocols. Moreover, jamming devices are getting smarter, making them difficult to detect. Forward error correction, which adds redundancy to data, is commonly deployed to protect communications against the deleterious effects of channel noise. Soft-information error correction decoders obtain reliability information from the receiver to inform their decoding, but in the presence of a jammer such information is misleading and results in degraded error correction performance. As decoders assume noise occurs independently to each bit, a bursty jammer will lead to greater degradation in performance than a non-bursty one. Here we establish, however, that such temporal dependencies can aid inferences on which bits have been subjected to jamming, thus enabling counter-measures. In particular, we introduce a pre-decoding processing step that updates log-likelihood ratio (LLR) reliability information to reflect inferences in the presence of a jammer, enabling improved decoding performance for any soft detection decoder. The proposed method requires no alteration to the decoding algorithm. Simulation results show that the method correctly infers a significant proportion of jamming in any received frame. Results with one particular decoding algorithm, the recently introduced ORBGRAND, show that the proposed method reduces the block-error rate (BLER) by an order of magnitude for a selection of codes, and prevents complete DoS at the receiver.

cs.IT↗

arXiv2022

IoT-Scan: Network Reconnaissance for the Internet of Things

Network reconnaissance is a core networking and security procedure aimed at discovering devices and their properties. For IP-based networks, several network reconnaissance tools are available, such as Nmap. For the Internet of Things (IoT), there is currently no similar tool capable of discovering devices across multiple protocols. In this paper, we present IoT-Scan, a universal IoT network reconnaissance tool. IoT-Scan is based on software defined radio (SDR) technology, which allows for a flexible software-based implementation of radio protocols. We present a series of passive, active, multi-channel, and multi-protocol scanning algorithms to speed up the discovery of devices with IoT-Scan. We benchmark the passive scanning algorithms against a theoretical traffic model based on the non-uniform coupon collector problem. We implement the scanning algorithms and compare their performance for four popular IoT protocols: Zigbee, Bluetooth LE, Z-Wave, and LoRa. Through extensive experiments with dozens of IoT devices, we demonstrate that our implementation experiences minimal packet losses and achieves performance near the theoretical benchmark. Using multi-protocol scanning, we further demonstrate a reduction of 70\% in the discovery times of Bluetooth and Zigbee devices in the 2.4\,GHz band and of LoRa and Z-Wave devices in the 900\,MHz band, compared to sequential passive scanning. We make our implementation and data available to the research community to allow independent replication of our results and facilitate further development of the tool.

eess.SY↗

arXiv2020

Denial-of-Service Attacks on C-V2X Networks

Cellular Vehicle-to-Everything (C-V2X) networks are increasingly adopted by automotive original equipment manufacturers (OEMs). C-V2X, as defined in 3GPP Release 14 Mode 4, allows vehicles to self-manage the network in absence of a cellular base-station. Since C-V2X networks convey safety-critical messages, it is crucial to assess their security posture. This work contributes a novel set of Denial-of-Service (DoS) attacks on C-V2X networks operating in Mode 4. The attacks are caused by adversarial resource block selection and vary in sophistication and efficiency. In particular, we consider "oblivious" adversaries that ignore recent transmission activity on resource blocks, "smart" adversaries that do monitor activity on each resource block, and "cooperative" adversaries that work together to ensure they attack different targets. We analyze and simulate these attacks to showcase their effectiveness. Assuming a fixed number of attackers, we show that at low vehicle density, smart and cooperative attacks can significantly impact network performance, while at high vehicle density, oblivious attacks are almost as effective as the more sophisticated attacks.

cs.CR↗

arXiv2020

Strategic Revenue Management of Preemptive versus Non-Preemptive Queues

Consider a two-class unobservable priority queue, with Poisson arrivals, generally distributed service, and strategic customers. Customers are charged a fee when joining the premium class. We analyze the maximum revenue achievable under the non-preemptive (NP) and preemptive-resume (PR) policies, and show that a provider is always better off implementing the PR policy. Further, the maximum revenue under PR is sometimes achieved when only a fraction of the customers join the premium class.

cs.GT↗

arXiv2020

Characterizing Orphan Transactions in the Bitcoin Network

Orphan transactions are those whose parental income-sources are missing at the time that they are processed. These transactions are not propagated to other nodes until all of their missing parents are received, and they thus end up languishing in a local buffer until evicted or their parents are found. Although there has been little work in the literature on characterizing the nature and impact of such orphans, it is intuitive that they may affect throughput on the Bitcoin network. This work thus seeks to methodically research such effects through a measurement campaign of orphan transactions on live Bitcoin nodes. Our data show that, surprisingly, orphan transactions tend to have fewer parents on average than non-orphan transactions. Moreover, the salient features of their missing parents are a lower fee and larger size than their non-orphan counterparts, resulting in a lower transaction fee per byte. Finally, we note that the network overhead incurred by these orphan transactions can be significant, exceeding 17% when using the default orphan memory pool size (100 transactions). However, this overhead can be made negligible, without significant computational or memory demands, if the pool size is merely increased to 1000 transactions.

cs.CR↗

arXiv2020

Social Welfare and Price of Anarchy in Preemptive Priority Queues

Consider an unobservable $M|G|1$ queue with preemptive-resume scheduling and two priority classes. Customers are strategic and may join the premium class for a fee. We analyze the resulting equilibrium outcomes, equilibrium stability, and social welfare. We find that for service distributions with coefficient of variation greater than 1, there exists a unique and stable mixed equilibrium at low loads. We also establish a tight bound on the price of anarchy, which is $4/3$.

cs.GT↗

arXiv2019

Modeling Information Propagation in General V2V-enabled Transportation Networks

V2V technologies bridge two infrastructures: the communications infrastructure and the transportation infrastructure. These infrastructures are interconnected and interdependent. On the one hand, the communications network enables V2V interactions, while, on the other hand, the density of vehicles on the roadway enabled with V2V and the level of congestion on the roadway determine the speed and quality of communications between vehicles and infrastructure. The V2V technology is expected to contribute significantly to the growth of shared mobility, in turn, receives a significant boost from the deployment of a large number of connected vehicles in shared mobility services, provided challenges towards the deployment can be overcome. Vehicle mobility patterns and communication conditions are not only heterogeneous, but they also evolve constantly, leading to dynamic coupling between the communication and the transportation infrastructure. We consider the communication of messages amongst the vehicles in a transportation network, and estimate how quickly messages spread under different conditions of traffic density (traffic congestion, the presence of an accident, and time of day such as morning and evening rush hour) and communication conditions. We developed a continuous-time Markov chain to describe the information propagation process through enabled vehicles. Our models converge to a solution of a set of clustered epidemiological differential equations which lend itself to fast computation. We then demonstrate the applicability of this model in various scenarios: both real-world scenarios and hypothesized scenarios of outages and system perturbations. We find that our models match actual trajectory data with very little error, demonstrating the applicability of our models to study the spread of information through a network of connected vehicles.

cs.SI↗

arXiv2018

Equilibrium and Learning in Queues with Advance Reservations

Consider a multi-class preemptive-resume $M/D/1$ queueing system that supports advance reservations (AR). In this system, strategic customers must decide whether to reserve a server in advance (thereby gaining higher priority) or avoid AR. Reserving a server in advance bears a cost. In this paper, we conduct a game-theoretic analysis of this system, characterizing the equilibrium strategies. Specifically, we show that the game has two types of equilibria. In one type, none of the customers makes reservation. In the other type, only customers that realize early enough that they will need service make reservations. We show that the types and number of equilibria depend on the parameters of the queue and on the reservation cost. Specifically, we prove that the equilibrium is unique if the server utilization is below 1/2. Otherwise, there may be multiple equilibria depending on the reservation cost. Next, we assume that the reservation cost is a fee set by the provider. In that case, we show that the revenue maximizing fee leads to a unique equilibrium if the utilization is below 2/3, but multiple equilibria if the utilization exceeds 2/3. Finally, we study a dynamic version of the game, where users learn and adapt their strategies based on observations of past actions or strategies of other users. Depending on the type of learning (i.e., action learning vs.\ strategy learning), we show that the game converges to an equilibrium in some cases, while it cycles in other cases.

cs.GT↗

arXiv2018

Cascading Denial of Service Attacks on Wi-Fi Networks

We unveil the existence of a vulnerability in Wi-Fi, which allows an adversary to remotely launch a Denial-of-Service (DoS) attack that propagates both in time and space. This vulnerability stems from a coupling effect induced by hidden nodes. Cascading DoS attacks can congest an entire network and do not require the adversary to violate any protocol. We demonstrate the feasibility of such attacks through experiments with real Wi-Fi cards, extensive ns-3 simulations, and theoretical analysis. The simulations show that the attack is effective both in networks operating under fixed and varying bit rates, as well as ad hoc and infrastructure modes. To gain insight into the root-causes of the attack, we model the network as a dynamical system and analyze its limiting behavior. The model predicts that a phase transition (and hence a cascading attack) is possible when the retry limit parameter of Wi-Fi is greater or equal to 7, and explicitly characterizes the phase transition region in terms of the system parameters.

cs.NI↗