arXiv subjects

Rémi Van Boxem

Publications and source records attributed to Rémi Van Boxem.

2 recordsLinked to original sources

arXiv2026

Shy Guys: A Light-Weight Approach to Detecting Robots on Websites

Automated bots now account for roughly half of all web requests, and an increasing number deliberately spoof their identity to either evade detection or to not respect robots.txt. Existing countermeasures are either resource-intensive (JavaScript challenges, CAPTCHAs), cost-prohibitive (commercial solutions), or degrade the user experience. This paper proposes a lightweight, passive approach to bot detection that combines user-agent string analysis with favicon-based heuristics, operating entirely on standard web server logs with no client-side interaction. We evaluate the method on over 4.6 million requests containing 54,945 unique user-agent strings collected from website hosted all around the earth. Our approach detects 67.7% of bot traffic while maintaining a false-positive rate of 3%, outperforming state of the art (less than 20%). This method can serve as a first line of defence, routing only genuinely ambiguous requests to active challenges and preserving the experience of legitimate users.

cs.NI↗

arXiv2025

The Forest Behind the Tree: Revealing Hidden Smart Home Communication Patterns

The widespread use of Smart Home devices has attracted significant research interest in understanding their behavior within home networks. Unlike general-purpose computers, these devices exhibit relatively simple and predictable network activity patterns. However, previous studies have primarily focused on normal network conditions, overlooking potential hidden patterns that emerge under challenging conditions. Discovering these hidden flows is crucial for assessing device robustness. This paper addresses this gap by presenting a framework that systematically and automatically reveals these hidden communication patterns. By actively disturbing communication and blocking observed traffic, the framework generates comprehensive profiles structured as behavior trees, uncovering flows that are missed by more shallow methods. This approach was applied to ten real-world devices, identifying 254 unique flows, with over 27% only discovered through this new method. These insights enhance our understanding of device robustness and can be leveraged to improve the accuracy of network security measures.

cs.NI↗