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Distributed large independent sets in one round on bounded-independence graphs
(Springer Verlag, 2015) Halldórsson, Magnús M.; Konrad, Christian; Moses, Yoram; Department of Computer Science
We present a randomized one-round, single-bit messages, distributed algorithm for the maximum independent set problem in polynomially bounded-independence graphs with poly-logarithmic approximation factor. Bounded-independence graphs capture various models of wireless networks such as the unit disc graphs model and the quasi unit disc graphs model. For instance, on unit disc graphs, our achieved approximation ratio is (Formula presented.). A starting point of our work is an extension of Turán’s bound for independent sets by Caro and Wei which states that every graph G = (V,E) contains an independent set of size at least (Formula presented.), where degG(v) denotes the degree of v in G. Alon and Spencer’s proof of the Caro-Wei bound in [1] suggests a randomized distributed oneround algorithm that outputs an independent set of expected size equal to β(G), using messages of sizes O(log n), where n is the number of vertices of the input graph. To achieve our main result, we show that β(G) gives poly-logarithmic approximation ratios for polynomially boundedindependence graphs. Then, for O(1)-claw free graphs (which include graphs of bounded-independence), we show that using a different algorithm, an independent set of expected size Θ(β(G)) can be computed in one round using single bit messages, thus reducing the communication cost to an absolute minimum. Last, in general graphs, β(G) may only give an Ω(n)-approximation. We show, however, that this is best possible for one-round algorithms: We show that each such distributed algorithm (possibly randomized) has an approximation ratio of Ω(n) on general graphs.
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Harnessing Selective State Space Models to Enhance Semianalytical Design of Fabrication-Ready Multilayered Huygens' Metasurfaces : Part I - Field-based Semianalytical Synthesis
(2026-03-04) Marcus, Sherman W.; Nissan, Natanel; Killamsetty, Vinay K.; Yadav, Ravi; Raviv, Dan; Giryes, Raja; Epstein, Ariel; Verkfræðideild
Planar metasurfaces can profoundly control electromagnetic scattering. At microwave frequencies, such devices are typically implemented using multilayer cascades of patterned metallic sheets, whose design often requires time-consuming full-wave optimization. Here, we extend analytical models originally developed for sparse loaded-wire metagratings to accurately describe densely packed Jerusalem-cross meta-atoms embedded in standard printed circuit board (PCB) dielectric stacks. The model captures both near- and far-field coupling within and between layers, enabling efficient prediction of the dual-polarized response. Using this framework, we identify highly transmissive meta-atoms whose phase is controlled by the leg lengths of the Jerusalem crosses (microscopic design stage). This (phase)-(leg-length) "lookup table" allows rapid synthesis of Huygens' metasurfaces (macroscopic design stage), demonstrated through a full-wave-validated metalens exhibiting low-reflection beam manipulation. Notably, we implement a judicious scaling method to further extend the model to predict wideband meta-atom responses. In the companion paper (Part II), a hybrid machine-learning approach leverages this semianalytical framework to enhance accuracy without requiring the conventional exhaustive full-wave training, enabling ultrafast inverse design across the full parameter space. Overall, the presented methodology -- the standalone semianlytical scheme (Part I) and the machine-learning enhanced version (Part II) -- establishes an effective open-source toolkit for versatile, rapid, and highly accurate synthesis of fabrication-ready dual-polarized transmissive Huygens' meta-atoms and metasurfaces.
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“Are Crowdsourcing Platforms Reliable for Video Game-related Research?” A Case Study on Amazon Mechanical Turk
(Association for Computing Machinery, Inc, 2024-10-14) Eisele, Linus; Apruzzese, Giovanni; Department of Computer Science
Video games are becoming increasingly popular in research, and abundant prior work has investigated this domain by means of user studies. However, carrying out user studies whose population encompasses a large and diverse set of participants is challenging. Crowdsourcing platforms, such as Amazon Mechanical Turk (AMT), represent a cost-effective solution to address this problem. Yet, prior efforts scrutinizing the data-quality (unrelated to gaming) collected via AMT raises a concern: is AMT reliable for game studies? In this paper, we are the first to tackle this question. We carry out three user studies (n=302) through which we evaluate the overall validity of the responses—pertaining to 14 popular video games—we received via AMT. We adopt strict verification mechanisms, which are trivial to “bypass” by real gamers, but costly for non-gamers. We found that the percentage of valid responses ranges from 5% (for WoW) to 28% (for PUBG). We hence advocate future research to carefully scrutinize the validity of responses collected via AMT.
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"Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages
(Association for Computing Machinery, Inc, 2024-05-13) Yuan, Ying; Hao, Qingying; Apruzzese, Giovanni; Conti, Mauro; Wang, Gang; Department of Computer Science
Machine learning based phishing website detectors (ML-PWD) are a critical part of today's anti-phishing solutions in operation. Unfortunately, ML-PWD are prone to adversarial evasions, evidenced by both academic studies and analyses of real-world adversarial phishing webpages. However, existing works mostly focused on assessing adversarial phishing webpages against ML-PWD, while neglecting a crucial aspect: investigating whether they can deceive the actual target of phishing - -the end users. In this paper, we fill this gap by conducting two user studies (n=470) to examine how human users perceive adversarial phishing webpages, spanning both synthetically crafted ones (which we create by evading a state-of-the-art ML-PWD) as well as real adversarial webpages (taken from the wild Web) that bypassed a production-grade ML-PWD. Our findings confirm that adversarial phishing is a threat to both users and ML-PWD, since most adversarial phishing webpages have comparable effectiveness on users w.r.t. unperturbed ones. However, not all adversarial perturbations are equally effective. For example, those with added typos are significantly more noticeable to users, who tend to overlook perturbations of higher visual magnitude (such as replacing the background). We also show that users' self-reported frequency of visiting a brand's website has a statistically negative correlation with their phishing detection accuracy, which is likely caused by overconfidence. We release our resources.
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Addressing Adversarial Attacks Against Security Systems Based on Machine Learning
(NATO CCD COE Publications, 2019-05) Apruzzese, Giovanni; Colajanni, Michele; Ferretti, Luca; Marchetti, Mirco; Minarik, Tomas; Alatalu, Siim; Biondi, Stefano; Signoretti, Massimiliano; Tolga, Ihsan; Visky, Gabor; Department of Computer Science
Machine-learning solutions are successfully adopted in multiple contexts but the application of these techniques to the cyber security domain is complex and still immature. Among the many open issues that affect security systems based on machine learning, we concentrate on adversarial attacks that aim to affect the detection and prediction capabilities of machine-learning models. We consider realistic types of poisoning and evasion attacks targeting security solutions devoted to malware, spam and network intrusion detection. We explore the possible damages that an attacker can cause to a cyber detector and present some existing and original defensive techniques in the context of intrusion detection systems. This paper contains several performance evaluations that are based on extensive experiments using large traffic datasets. The results highlight that modern adversarial attacks are highly effective against machine-learning classifiers for cyber detection, and that existing solutions require improvements in several directions. The paper paves the way for more robust machine-learning-based techniques that can be integrated into cyber security platforms.

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