arXiv subjects

Andrea Pferscher

Publications and source records attributed to Andrea Pferscher.

6 recordsLinked to original sources

arXiv2026

Incremental Fingerprinting in an Open World

Network protocol fingerprinting is used to identify a protocol implementation by analyzing its input-output behavior. Traditionally, fingerprinting operates under a closed-world assumption, where models of all implementations are assumed to be available. However, this assumption is unrealistic in practice. When this assumption does not hold, fingerprinting results in numerous misclassifications without indicating that a model for an implementation is missing. Therefore, we introduce an open-world variant of the fingerprinting problem, where not all models are known in advance. We propose an incremental fingerprinting approach to solve the problem by combining active automata learning with closed-world fingerprinting. Our approach quickly determines whether the implementation under consideration matches an available model using fingerprinting and conformance checking. If no match is found, it learns a new model by exploiting the structure of available models. We prove the correctness of our approach and improvements in asymptotic complexity compared to naive baselines. Moreover, experimental results on a variety of protocols demonstrate a significant reduction in misclassifications and interactions with these black-boxes.

cs.CR↗

arXiv2026

ORB: An Open Radio Buoy for Coastal Water Measurements

Oceanographic instrumentation technology is currently under rapid transition towards increasingly open-source technology. Open-source buoys compete with commercial and closed-source buoys both in price, functionality and availability. Long-range radio (LoRa) is a communication technology which is inexpensive both in terms of data transfer cost and power without the need for pre-existing infrastructure. In this paper, we present ORB, an open-source drifter buoy using LoRa for coastal water measurements. ORB is designed to be reliable, low-cost, modifiable and power efficient. We present validation experiments demonstrating that ORB can achieve a radio telemetry range of five kilometers, and has an expected battery lifetime of up to seven months, owing largely to a the radio telemetry which operates at the low power consumption of 7 mA. Finally, we discuss the role and contribution of ORB in the space of open-source instrumentation and ocean modeling.

physics.ao-ph↗

arXiv2026

Attribution-based Explanations for Markov Decision Processes

Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on attributing importance to static input features at a single point in time, and thus fail to generalize to sequential decision-making settings. This paper fills this gap by introducing techniques to generate attribution-based explanations for Markov Decision Processes (MDPs). We give a formal characterization of what attributions should represent in MDPs, focusing on explanations that assign importance scores to both individual states and execution paths. We show how importance scores can be computed by leveraging techniques for strategy synthesis, enabling the efficient computation of these scores despite the non-determinism inherent in an MDP. We evaluate our approach on five case-studies, demonstrating its utility in providing interpretable insights into the logic of sequential decision-making agents.

cs.AI↗

arXiv2026

Automata Learning versus Process Mining: The Case for User Journeys

With the servitization of business, understanding how users experience services becomes a crucial success factor for companies. Therefore, there is a need to include feedback from user experiences in the software engineering process. Behavioral models of user journeys, describing how users experience their interaction with a service, can provide insights and potentially improve services. In this paper, we investigate techniques that allow the automatic generation of behavioral models from user interactions with a service, recorded in an event log. We first compare two established techniques that generate behavioral models from a given event log: automata learning and process mining. Afterward, we present a novel, hybrid method that combines both automata learning and process mining methods to overcome their limitations. For the existing techniques, we present methods to learn models of user journeys and evaluate the accuracy of the resulting models. We then compare these techniques with our novel method for the automatic extraction of user journey models from the event logs of digital services. We assess the practical applicability of all techniques by evaluating real-world applications. Our results show that process mining techniques rely on expert knowledge, while automata learning techniques depend on the distribution of events in the given event log. We further show that the proposed hybrid technique combines the strengths of both process mining and automata learning, automatically selecting the best method and parameter settings for a given event log to learn very accurate models.

cs.SE↗

arXiv2023

Fingerprinting and Analysis of Bluetooth Devices with Automata Learning

Automata learning is a technique to automatically infer behavioral models of black-box systems. Today's learning algorithms enable the deduction of models that describe complex system properties, e.g., timed or stochastic behavior. Despite recent improvements in the scalability of learning algorithms, their practical applicability is still an open issue. Little work exists that actually learns models of physical black-box systems. To fill this gap in the literature, we present a case study on applying automata learning on the Bluetooth Low Energy (BLE) protocol. It shows that not only the size of the system limits the applicability of automata learning. Also, the interaction with the system under learning creates a major bottleneck that is rarely discussed. In this article, we propose a general automata learning architecture for learning a behavioral model of the BLE protocol implemented by a physical device. With this framework, we can successfully learn the behavior of six investigated BLE devices. Furthermore, we extended the learning technique to learn security critical behavior, e.g., key-exchange procedures for encrypted communication. The learned models depict several behavioral differences and inconsistencies to the BLE specification. This shows that automata learning can be used for fingerprinting black-box devices, i.e., characterizing systems via their specific learned models. Moreover, learning revealed a crashing scenario for one device.

cs.FL↗

arXiv2022

Active vs. Passive: A Comparison of Automata Learning Paradigms for Network Protocols

Active automata learning became a popular tool for the behavioral analysis of communication protocols. The main advantage is that no manual modeling effort is required since a behavioral model is automatically inferred from a black-box system. However, several real-world applications of this technique show that the overhead for the establishment of an active interface might hamper the practical applicability. Our recent work on the active learning of Bluetooth Low Energy (BLE) protocol found that the active interaction creates a bottleneck during learning. Considering the automata learning toolset, passive learning techniques appear as a promising solution since they do not require an active interface to the system under learning. Instead, models are learned based on a given data set. In this paper, we evaluate passive learning for two network protocols: BLE and Message Queuing Telemetry Transport (MQTT). Our results show that passive techniques can correctly learn with less data than required by active learning. However, a general random data generation for passive learning is more expensive compared to the costs of active learning.

cs.FL↗