All Years Seminars
[INMA] 2025-10-21 (14:00) : Over-Approximation Methods for Safe Control: Lifting and Preview Information
At Euler building (room A.002)
Speaker :
Aspeel, Antoine
Abstract :
Ensuring safety in the control of nonlinear systems is a central challenge in control theory. Over-approximations address this by replacing a deterministic nonlinear system with a nondeterministic (piecewise) linear one, enabling the use of control synthesis techniques with formal guarantees for the original dynamics.
The presentation will cover two recent approaches that reduce conservatism in over-approximations. Lifted over-approximations represent the system in higher dimension, providing additional degrees of freedom. Over-approximations with preview reinterpret the approximation error as input-dependent preview information, leading to policies that depend jointly on the state and the error. The resulting concretization problem—recovering a valid input for the true system from such a policy—is formulated as a fixed-point equation, enabling efficient computation.
[INMA] 2025-10-16 (13:00) : Malware Detection with Machine Learning: Challenges and Perspectives
At Shannon room, Maxwell building
Speaker :
Serena Lucca (ICTEAM)
,
and
Samy Bettaied (ICTEAM)
Abstract : This presentation explores some challenges and perspectives of applying machine learning to malware detection, drawing insights from two complementary studies. The first investigates the surprising effectiveness of simple models—such as One-Rule and AdaBoost—on state-of-the-art malware detection datasets, revealing that a small subset of dominant features often drives classification performance, leading to minimal differences between simple and deep learning approaches. The second study provides a systematic comparison of tabular and graph-based feature representations under unified conditions, evaluating their trade-offs in computational cost, detection accuracy, and robustness to adversarial attacks. Together, these works question the common assumption that complex models or sophisticated feature types always yield superior results, and instead highlight the importance of understanding feature dominance, dataset biases, and practical constraints in real-world malware detection.
[INGI] 2025-10-16 (13:00) : Malware Detection with Machine Learning: Challenges and Perspectives
At Shannon, Maxwell a.105
Speaker :
Serena Lucca (ICTEAM)
,
and
Samy Bettaied (ICTEAM)
Abstract : This presentation explores some challenges and perspectives of applying machine learning to malware detection, drawing insights from two complementary studies. The first investigates the surprising effectiveness of simple models—such as One-Rule and AdaBoost—on state-of-the-art malware detection datasets, revealing that a small subset of dominant features often drives classification performance, leading to minimal differences between simple and deep learning approaches. The second study provides a systematic comparison of tabular and graph-based feature representations under unified conditions, evaluating their trade-offs in computational cost, detection accuracy, and robustness to adversarial attacks. Together, these works question the common assumption that complex models or sophisticated feature types always yield superior results, and instead highlight the importance of understanding feature dominance, dataset biases, and practical constraints in real-world malware detection.
[INMA] 2025-10-15 (14:00) : Internal Optimization Seminar
At Euler building (room A.002)
Speaker :
No author specified
Abstract : This is a weekly seminar which explores cutting-edge research and applications in mathematical optimization, spanning theory, algorithms, and real-world problem-solving. Each session features talks from leading researchers, practitioners, or graduate students, covering various topics. Attendees include mathematicians, engineers, and data scientists — all united by a passion for optimization. Open to all. No registration required.
[INGI] 2025-10-15 (10:00) : Autonomous Systems under AReST: Advanced Revelation of Segment Routing Tunnels
At Shannon, Maxwell a.105
Speaker :
Florian Dekinder (ULiège)
Abstract : Segment Routing (Sr), an advanced source routing mechanism, is a promising technology with a wide range of applications that has already gained traction from hardware vendors, network operators, and researchers alike. However, despite the abundance of activity surrounding Sr, little is known about how to gauge Sr deployment and its usage by operators.
This paper introduces a methodology, called AReST (Advanced Revelation of Segment Routing Tunnels), for revealing the presence of Sr with Mpls as forwarding plane (Sr-Mpls). AReST relies on standard measurement tools, like traceroute and fingerprinting, and post-processes the collected data for highlighting evidence of Sr-Mpls. Our results show that AReST is efficient in revealing the presence of Sr-Mpls in various autonomous systems, obtaining a perfect precision on our ground truth directly obtained from an operator. We also make a preliminary characterization of the Sr-Mpls deployment and show that it is commonly deployed within Content, Transit, and Tier-1 providers and, occasionally, in interworking with classic Mpls. The data collected, as well as our source code, is available to the research community.
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L4.05.01
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+32 10 47 80 36
secretaire-inma@uclouvain.be
Mon – Fri 9:00A.M. – 5:00P.M.
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