Zobrazeno 1 - 10
of 127
pro vyhledávání: '"Menasche, Daniel"'
This paper presents an approach to managing access to Content Delivery Networks (CDNs), focusing on combating the misuse of tokens through performance analysis and statistical access patterns. In particular, we explore the impact of token sharing on
Externí odkaz:
http://arxiv.org/abs/2410.05642
Autor:
Patel, Kajal, Shafiq, Zubair, Nogueira, Mateus, Menasché, Daniel Sadoc, Lovat, Enrico, Kashif, Taimur, Woiwood, Ashton, Martins, Matheus
Many organizations rely on Threat Intelligence (TI) feeds to assess the risk associated with security threats. Due to the volume and heterogeneity of data, it is prohibitive to manually analyze the threat information available in different loosely st
Externí odkaz:
http://arxiv.org/abs/2409.07709
Autor:
Eshwar, S. R., Felipe, Lucas Lopes, Reiffers-Masson, Alexandre, Menasché, Daniel Sadoc, Thoppe, Gugan
Load balancing and auto scaling are at the core of scalable, contemporary systems, addressing dynamic resource allocation and service rate adjustments in response to workload changes. This paper introduces a novel model and algorithms for tuning load
Externí odkaz:
http://arxiv.org/abs/2406.14141
Similarity caching allows requests for an item to be served by a similar item. Applications include recommendation systems, multimedia retrieval, and machine learning. Recently, many similarity caching policies have been proposed, like SIM-LRU and RN
Externí odkaz:
http://arxiv.org/abs/2309.12149
Autor:
Moreno-Vera, Felipe, Nogueira, Mateus, Figueiredo, Cainã, Menasché, Daniel Sadoc, Bicudo, Miguel, Woiwood, Ashton, Lovat, Enrico, Kocheturov, Anton, de Aguiar, Leandro Pfleger
This paper proposes a machine learning-based approach for detecting the exploitation of vulnerabilities in the wild by monitoring underground hacking forums. The increasing volume of posts discussing exploitation in the wild calls for an automatic ap
Externí odkaz:
http://arxiv.org/abs/2308.02581
Indicators of Compromise (IOCs), such as IP addresses, file hashes, and domain names associated with known malware or attacks, are cornerstones of cybersecurity, serving to identify malicious activity on a network. In this work, we leverage real data
Externí odkaz:
http://arxiv.org/abs/2307.16852
Effective resource allocation in sensor networks, IoT systems, and distributed computing is essential for applications such as environmental monitoring, surveillance, and smart infrastructure. Sensors or agents must optimize their resource allocation
Externí odkaz:
http://arxiv.org/abs/2307.06442
Similarity caching allows requests for an item \(i\) to be served by a similar item \(i'\). Applications include recommendation systems, multimedia retrieval, and machine learning. Recently, many similarity caching policies have been proposed, but st
Externí odkaz:
http://arxiv.org/abs/2209.03174
We study how the amount of correlation between observations collected by distinct sensors/learners affects data collection and collaboration strategies by analyzing Fisher information and the Cramer-Rao bound. In particular, we consider a simple sett
Externí odkaz:
http://arxiv.org/abs/2206.00111
Autor:
Modina, Naresh, El-Azouzi, Rachid, De Pellegrini, Francesco, Menasche, Daniel Sadoc, Figueiredo, Rosa
The widespread adoption of 5G cellular technology will evolve as one of the major drivers for the growth of IoT-based applications. In this paper, we consider a Service Provider (SP) that launches a smart city service based on IoT data readings: in o
Externí odkaz:
http://arxiv.org/abs/2201.07615