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Shape optimization of trawl-doors using variable-fidelity models and space mapping
(2015) Jonsson, Ingi M.; Leifsson, Leifur; Koziel, Slawomir; Tesfahunegn, Yonatan A.; Bekasiewicz, Adrian; Department of Engineering
Trawl-doors have a large influence on the fuel consumption of fishing vessels. Design and optimization of trawl-doors using computational models are key factors in minimizing the fuel consumption. This paper presents an efficient optimization algorithm for the design of trawl-door shapes using computational fluid dynamic models. The approach is iterative and uses variable-fidelity models and space mapping. The algorithm is applied to the design of a multi-element trawl-door, involving four design variables controlling the angle of attack and the slat position and orientation. The results demonstrate that a satisfactory design can be obtained at a cost of a few iterations of the algorithm. Compared with direct optimization of the high-fidelity model and local response surface surrogate models, the proposed approach requires 79% less computational time while, at the same time, improving the design significantly (over 12% increase in the lift-to-drag ratio).
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When Adversarial Perturbations meet Concept Drift : an Exploratory Analysis on ML-NIDS
(Association for Computing Machinery, Inc, 2024-11-22) Apruzzese, Giovanni; Fass, Aurore; Pierazzi, Fabio; Department of Computer Science
We scrutinize the effects of “blind” adversarial perturbations against machine learning (ML)-based network intrusion detection systems (NIDS) affected by concept drift. There may be cases in which a real attacker – unable to access and hence unaware that the ML-NIDS is weakened by concept drift – attempts to evade the ML-NIDS with data perturbations. It is currently unknown if the cumulative effect of such adversarial perturbations and concept drift leads to a greater or lower impact on ML-NIDS. In this “open problem” paper, we seek to investigate this unusual, but realistic, setting—we are not interested in perfect knowledge attackers. We begin by retrieving a publicly available dataset of documented network traces captured in a real, large (>300 hosts) organization. Overall, these traces include several years of raw traffic packets—both benign and malicious. Then, we adversarially manipulate malicious packets with problem-space perturbations, representing a physically realizable attack. Finally, we carry out the first exploratory analysis focused on comparing the effects of our “adversarial examples” with their respective unperturbed malicious variants in concept-drift scenarios. Through two case studies (a “short-term” one of 8 days; and a “long-term” one of 4 years) encompassing 48 detector variants, we find that, although our perturbations induce a lower detection rate in concept-drift scenarios, some perturbations yield adverse effects for the attacker in intriguing use cases. Overall, our study shows that the topics we covered are still an open problem which require a re-assessment from future research.
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"what is the Problem Space?" Defining Host-space Adversarial Perturbations against Network Intrusion Detection Systems
(Association for Computing Machinery, Inc, 2026-06-01) Verkerken, Miel; D'Hooge, Laurens; Volckaert, Bruno; De Turck, Filip; Apruzzese, Giovanni; Department of Computer Science
Network Intrusion Detection Systems (NIDS) are now increasingly leveraging Machine Learning (ML) techniques to detect malicious network activities. Numerous papers have scrutinized the security of ML-based NIDS (ML-NIDS) by testing them against various attacks involving adversarial perturbations. The findings were oftentimes worrying: by making imperceptible changes to a given input, powerful ML models would be bypassed. In this context, we took a step back and wondered: where (i.e., in what "space") have these perturbations been applied?We argue that real-world adversaries can apply adversarial perturbations only by operating on the hosts they can control - a concept which we define as host-space perturbations. To some, such an observation may seem trivial. And yet, through a systematic literature review (n=316), we found that prior work applied perturbations by manipulating pre-collected datapoints (e.g., a packet captured by the router, or a network flow analysed by the ML-NIDS). Such operations, while not impossible, may be outside the reach of an attacker who can only control some (unprivileged) hosts in a network. Hence, to demonstrate how to craft host-space perturbations and study some of their effects, we experimented on well-known benchmarks and a real-world network. We show that ML-NIDS that can detect the SSH-bruteforcing attempts launched via a given command string cannot detect any attempt launched by changing a single character of such a string. We then examined how such a minuscule change in the "problem space"(i.e., the attacker's host) can lead to devastating effects on the "feature space". We derive lessons learned on how to practically assess host-space perturbations. Our stance is that the security of ML-NIDS should be re-assessed.
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“We provide our resources in a dedicated repository” Surveying the Transparency of HICSS publications
(IEEE Computer Society, 2025) Pekaric, Irdin; Apruzzese, Giovanni; Bui, Tung X.; Department of Computer Science
Every day, new discoveries are made by researchers from all across the globe and fields. HICSS is a flagship venue to present and discuss such scientific advances. Yet, the activities carried out for any given research can hardly be fully contained in a single document of a few pages-the “paper.” Indeed, any given study entails data, artifacts, or other material that is crucial to truly appreciate the contributions claimed in the corresponding paper. External repositories (e.g., GitHub) are a convenient tool to store all such resources so that future work can freely observe and build upon them-thereby improving transparency and promoting reproducibility of research as a whole. In this work, we scrutinize the extent to which papers recently accepted to HICSS leverage such repositories to provide supplementary material. To this end, we collect all the 5579 papers included in HICSS proceedings from 2017-2024. Then, we identify those entailing either human subject research (850) or technical implementations (737), or both (147). Finally, we review their text, examining how many include a link to an external repository-and, inspect its contents. Overall, out of 2028 papers, only 3% have a functional and publicly available repository that is usable by downstream research. We release all our tools.
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The Impact of Emerging Phishing Threats : Assessing Quishing and LLM-generated Phishing Emails against Organizations
(Association for Computing Machinery, 2025-08-24) Weinz, Marie; Zannone, Nicola; Allodi, Luca; Apruzzese, Giovanni; Department of Computer Science
Modern organizations are persistently targeted by phishing emails. Despite advances in detection systems and widespread employee training, attackers continue to innovate, posing ongoing threats. Two emerging vectors stand out in the current landscape: QR-code baits and LLM-enabled pretexting. Yet, little is known about the effectiveness of current defenses against these attacks, particularly when it comes to real-world impact on employees. This gap leaves uncertainty around to what extent related countermeasures are justified or needed. Our work addresses this issue. We conduct three phishing simulations across organizations of varying sizes - from small-medium businesses to a multinational enterprise. In total, we send over 71k emails targeting employees, including: a "traditional"phishing email with a click-through button; a nearly-identical "quishing"email with a QR code instead; and a phishing email written with the assistance of an LLM and open-source intelligence. Our results show that quishing emails have the same effectiveness as traditional phishing emails at luring users to the landing webpage - which is worrying, given that quishing emails are much harder to identify even by operational detectors. We also find that LLMs can be very good "social engineers": in one company, over 30% of the emails opened led to visiting the landing webpage - a rate exceeding some prior benchmarks. Finally, we complement our study by conducting a survey across the organizations' employees, measuring their "perceived"phishing awareness. Our findings suggest a correlation between higher self-reported awareness and organizational resilience to phishing attempts.

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