Search

To protect against prefix hijacks, Resource Public Key Infrastructure (RPKI) has been standardized. To enjoy the security guarantees of RPKI validation, networks need to install a new component, the relying party validator, which fetches and validates RPKI objects and provides them to border routers. However, recent work shows that relying parties experience failures when retrieving RPKI objects and are vulnerable to attacks, all of which can disable RPKI validation. Therefore even the few adopters are not necessarily secure. We make the first proposal that significantly improves the resilience and security of RPKI. We develop BRP, a Byzantine-Secure relying party implementation. In BRP the relying party nodes redundantly validate RPKI objects and reach a global consensus through voting. BRP provides an RPKI equivalent of public DNS, removing the need for networks to install, operate, and upgrade their own relying party instances while avoiding the need to trust operators of BRP nodes. We show through simulations and experiments that BRP, as an intermediate RPKI service, results in less load on RPKI publication points and a robust output despite RPKI repository failures, jitter, and attacks. We engineer BRP to be fully backward compatible and readily deployable - it does not require any changes to the border routers and the RPKI repositories. We demonstrate that BRP can protect many networks transparently, with either a decentralized or centralized deployment. BRP can be set up as a network of decentralized volunteer deployments, similarly to NTP and TOR, where different operators participate in the peering process with their node, and provide resilient and secure relying party validation to the Internet. BRP can also be hosted by a single operator as a centralized service, e.g., on one cloud or CDN, and provides RPKI validation benefits even when hosted on a single network.

Recent works showed that it is feasible to hijack resources on cloud platforms. In such hijacks, attackers can take over released resources that belong to legitimate organizations. It was proposed that adversaries could abuse these resources to carry out attacks against customers of the hijacked services, e.g., through malware distribution. However, to date, no research has confirmed the existence of these attacks. We identify, for the first time, real-life hijacks of cloud resources. This yields a number of surprising and important insights. First, contrary to previous assumption that attackers primarily target IP addresses, our findings reveal that the type of resource is not the main consideration in a hijack. Attackers focus on hijacking records that allow them to determine the resource by entering freetext. The costs and overhead of hijacking such records are much lower than those of hijacking IP addresses, which are randomly selected from a large pool. Second, identifying hijacks poses a substantial challenge. Monitoring resource changes, e.g., changes in content, is insufficient, since such changes could also be legitimate. Retrospective analysis of digital assets to identify hijacks is also arduous due to the immense volume of data involved and the absence of indicators to search for. To address this challenge, we develop a novel approach that involves analyzing data from diverse sources to effectively differentiate between malicious and legitimate modifications. Our analysis has revealed 20,904 instances of hijacked resources on popular cloud platforms. While some hijacks are short-lived (up to 15 days), 1/3 persist for more than 65 days. We study how attackers abuse the hijacked resources and find that, in contrast to the threats considered in previous work, the majority of the abuse (75%) is blackhat search engine optimization., Comment: 17 pages, 29 figures, to be published in NSDI'24: Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation

This paper considers the filtering problem which consists in reconstructing the state of a dynamical system with partial observations coming from sensor measurements, and the knowledge that the dynamics are governed by a physical PDE model with unknown parameters. We present a filtering algorithm where the reconstruction of the dynamics is done with neural network approximations whose weights are dynamically updated using observational data. In addition to the estimate of the state, we also obtain time-dependent parameter estimations of the PDE parameters governing the observed evolution. We illustrate the behavior of the method in a one-dimensional KdV equation involving the transport of solutions with local support. Our numerical investigation reveals the importance of the location and number of the observations. In particular, it suggests to consider dynamical sensor placement.

We introduce a new Projected Rayleigh Quotient Iteration aimed at improving the convergence behaviour of classic Rayleigh Quotient iteration (RQI) by incorporating approximate information about the target eigenvector at each step. While classic RQI exhibits local cubic convergence for Hermitian matrices, its global behaviour can be unpredictable, whereby it may converge to an eigenvalue far away from the target, even when started with accurate initial conditions. This problem is exacerbated when the eigenvalues are closely spaced. The key idea of the new algorithm is at each step to add a complex-valued projection to the original matrix (that depends on the current eigenvector approximation), such that the unwanted eigenvalues are lifted into the complex plane while the target stays close to the real line, thereby increasing the spacing between the target eigenvalue and the rest of the spectrum. Making better use of the eigenvector approximation leads to more robust convergence behaviour and the new method converges reliably to the correct target eigenpair for a significantly wider range of initial vectors than does classic RQI. We prove that the method converges locally cubically and we present several numerical examples demonstrating the improved global convergence behaviour. In particular, we apply it to compute eigenvalues in a band-gap spectrum of a Sturm-Liouville operator used to model photonic crystal fibres, where the target and unwanted eigenvalues are closely spaced. The examples show that the new method converges to the desired eigenpair even when the eigenvalue spacing is very small, often succeeding when classic RQI fails., Comment: 23 pages, 6 figures, 1 table

The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system based on a swarm of nano-UAVs, characterized by a limited payload of 35 g and tightly constrained on-board sensing and computing capabilities. Each nano-UAV is equipped with four 64-pixel depth sensors that measure the relative distance to obstacles in four directions. The proposed system merges the information from the swarm and generates a coherent grid map without relying on any external infrastructure. The data fusion is performed using the iterative closest point algorithm and a graph-based simultaneous localization and mapping algorithm, running entirely on-board the UAV's low-power ARM Cortex-M microcontroller with just 192 kB of SRAM memory. Field results gathered in three different mazes from a swarm of up to 4 nano-UAVs prove a mapping accuracy of 12 cm and demonstrate that the mapping time is inversely proportional to the number of agents. The proposed framework scales linearly in terms of communication bandwidth and on-board computational complexity, supporting communication between up to 20 nano-UAVs and mapping of areas up to 180 m2 with the chosen configuration requiring only 50 kB of memory.

Terahertz time-domain spectroscopy and differential scanning calorimetry were used to study the role of the dynamics of biomolecules decoupled from solvent effects. Lyophilised sucrose exhibited steadily increasing absorption with temperature as anharmonic excitations commence as the system emerges from a deep minimum of the potential energy landscape where harmonic vibrations dominate. The polypeptide bacitracin and two globular proteins, lysozyme and human serum albumin, showed a more complex temperature dependence. Further analysis focused on the spectral signature below and above the boson peak. We found evidence for the onset of anharmonic motions that are characteristic for partial unfolding and molecular jamming in the dry biomolecules. The activation of modes of the protein molecules at temperatures comparable to the protein dynamical transition temperature was observed in the absence of hydration. No evidence for Fr\"ohlich coherence, postulated to facilitate biological function, was found in our experiments.

Probabilistic variants of Model Order Reduction (MOR) methods have recently emerged for improving stability and computational performance of classical approaches. In this paper, we propose a probabilistic Reduced Basis Method (RBM) for the approximation of a family of parameter-dependent functions. It relies on a probabilistic greedy algorithm with an error indicator that can be written as an expectation of some parameter-dependent random variable. Practical algorithms relying on Monte Carlo estimates of this error indicator are discussed. In particular, when using Probably Approximately Correct (PAC) bandit algorithm, the resulting procedure is proven to be a weak greedy algorithm with high probability. Intended applications concern the approximation of a parameter-dependent family of functions for which we only have access to (noisy) pointwise evaluations. As a particular application, we consider the approximation of solution manifolds of linear parameter-dependent partial differential equations with a probabilistic interpretation through the Feynman-Kac formula.