All Years Seminars
[INGI] 2025-12-11 (13:00) : Combinatorial Transition Testing in Dynamically Adaptive Systems: Implementation and Test Oracle
At Shannon, Maxwell a.105
Speaker :
Pierre Martou (ICTEAM)
Abstract : Due to the large number of possible interactions and transitions among features in dynamically adaptive systems, testing such systems poses significant challenges. To verify that such systems behave correctly, combinatorial interaction testing (CIT) can create concise test suites covering all valid pairs of features of such systems. While CIT claims to find all errors caused by two features, it does not cover certain errors occurring only for specific transitions between features. To address this issue we study the technique of Combinatorial Transition Testing (CTT), which includes both generation and detection of what we call behavioural transition errors. From an initial generation algorithm that combines both interaction and transition coverage but lacks scalability, we propose an optimised version that enables CTT even for hundreds of features. From a valid test suite covering all transitions, we complete our testing approach with a test oracle that detects all behavioural transition errors without any prior knowledge of the system's behaviour. After a comprehensive analysis over a large number of feature models, we conclude that size of CTT-generated test suites and test effort needed to use our test oracle are linearly correlated to CIT-generated ones and that CTT grows logarithmically in the number of features.
[INMA] 2025-12-09 (14:00) : Matroids are equitable
At EULER (room A.002)
Speaker :
László Végh (University of Bonn)
Abstract : We show that if the ground set of a matroid can be partitioned into k≥2 bases, then for any given subset S of the ground set, there is a partition into k bases such that the sizes of the intersections of the bases with S may differ by at most one, settling a conjecture by Fekete and Szabó from 2011.
In the talk, I will present the surprisingly simple proof, as well as some extensions and applicaitons in fair division. I will also give an overview of related questions on matroid basis exchanges. This is based on joint work with Hannaneh Akrami, Roshan Raj, and Siyue Liu.
[INGI] 2025-12-04 (13:00) : Empowering QUIC with Connection Delegation to Save Energy
At Shannon, Maxwell a.105
Speaker :
Vany Ingenzi (ICTEAM)
Abstract : The end-to-end principle is fundamental to the design of the Internet, requiring that hosts participating in a transport connection remain continuously capable of processing packets. However, this principle conflicts with the energy-saving needs of battery-powered wireless devices, which benefit from short and intermittent activity periods.
In this talk, we present a QUIC connection delegation mechanism that allows a client to transfer an active connection to a trusted third party by securely migrating its connection state. The third party then maintains the connection and handles data exchanges on behalf of the original client, allowing the client device to enter a power-saving mode. Our evaluation shows that QUIC connection delegation can reduce energy consumption over Wi-Fi by up to 55% when the smartphone can utilize its sleep mode, and by 25% when accounting for background traffic and an active display.
[INMA] 2025-12-02 (14:00) : Expanding BGP Data Horizons
At EULER (room A.002)
Speaker :
Cristel Pelsser (INGI/ICTEAM/UCL)
Abstract : BGP data collection platforms as currently architected face fundamental challenges that threaten their long-term sustainability: their data comes with enormous redundancy and yet dangerous visibility gaps. GILL is a new BGP routes collection platform that can collect routes from at least an order of magnitude more routers compared to existing platforms while limiting the increase in human effort and data volume. GILL’s key principle is an overshoot-and-discard collection scheme: Any AS can easily peer with GILL and export their routes. GILL offers a lossy compression algorithm that only stores the nonredundant routes as well as lossless compression leveraging redundancy in BGP attributes, in our new bgproutes.io platform. Our new mode of data selection and delivery enables to improve BGP data analysis such as topology mapping, AS ranking, and forged origin hijack detection.
We have built such a detector. DFOH is a system designed to detect forged-origin hijacks across the entire Internet. Forged-origin hijacks are typically malicious BGP hijacks where attackers manipulate the AS path of BGP messages to make them appear as legitimate routing updates. DFOH is particularly useful because the proposed BGP extensions for cryptographically verifying the validity of AS paths (e.g., BGPSec or ASPA) are challenging to deploy widely. With DFOH, operators can quickly and confidently determine when their traffic is being hijacked.
[INGI] 2025-11-27 (13:00) : Uncovering Malicious Persistence: Machine Learning-Based Detection of Windows Scheduled Tasks
At Shannon, Maxwell a.105
Speaker :
Khaled Rahal (ERM & ULB)
Abstract : Advanced Persistent Threats (APT) represent a serious security concern because they
carry out long-term and carefully planned attacks. While a lot of research has gone into finding
ways to detect these threats; one crucial area often gets less attention, namely the persistence
mechanisms that allow attackers to stay hidden and maintain access to systems over
time. In this work, we investigate scheduled tasks, a widely used persistence technique
in Windows environments, and analyze their role in APT operations. We conducted an
in-depth study of how attackers leverage scheduled tasks to maintain stealthy access and
execute malicious actions over time. We introduce Detecting APT Through Malicious
Scheduled Tasks (DAPTASK), an approach that leverages Sysmon log data, Word2Vecbased
feature representation, and Machine Learning (ML) classifiers to identify malicious
1scheduled tasks commonly used in APT persistence techniques. Our approach achieves
a high detection performance, with an F1-score of 95.19%. Furthermore, we provide a
labeled dataset, which can serve as a valuable resource for researchers developing APT
detection methods, the dataset and the code used are publicly available at https://gitlab.cylab.
be/cylab/daptask. Our approach enhances APT detection by addressing persistence
techniques, a critical yet often neglected attack vector.
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