arXiv subjects

Max Mühlhäuser

Publications and source records attributed to Max Mühlhäuser.

At least 19 recordsLinked to original sources

arXiv2026

PRISM: Predictive Runtime In-place Scaling and Model Selection for Edge Microservices

Latency-sensitive edge AI services must balance strict deadlines, output quality, and limited compute and energy budgets. However, static CPU provisioning wastes resources because inference cost varies substantially across inputs, model variants, and runtime conditions. We present PRISM, a prediction-guided runtime framework that jointly selects model variants and CPU allocations for containerized edge microservices. Using container-level energy monitoring and lightweight regression models, PRISM adapts each pipeline stage in place and minimizes predicted CPU-package energy under deadline, resource, and offline model-level Quality of Result (QoR) constraints. We evaluate PRISM on more than 52,000 requests in an Automatic License Plate Recognition (ALPR) pipeline with detection and recognition stages. For detection, PRISM reduces energy consumption by 36 % compared to the strongest static configuration while preserving a comparable success rate and using less than half of the average CPU allocation. For recognition, it reaches near-static-best performance with lower average CPU allocation. These results show that predictive in-place adaptation is a practical mechanism for making time-sensitive AI microservice pipelines more energy-efficient at the edge.

cs.DC↗

arXiv2026

PRICE: Pricing-based Resource Incentives for Quality-of-Result-aware Computing at the Edge

Edge nodes are capacity-constrained by design, yet many edge workloads can trade result quality for resource efficiency at runtime. Existing edge pricing mechanisms largely treat requests as fixed-configuration submissions and rarely exploit per-request quality flexibility under overload. We present PRICE, an incentive mechanism that couples a utilization-dependent price signal to per-request quality selection. As utilization increases, rising acceptance prices make resource-intensive variants less likely to be selected, shifting accepted requests toward lighter execution and allowing the node to serve significantly more requests while operating near capacity. Evaluation on real hardware under sustained overload shows that PRICE outperforms both fixed-allocation and dynamic-pricing baselines in accepted throughput and CPU utilization. The results are robust across pricing function families, task-duration distributions, and client populations. Result-quality flexibility is a powerful but underused control dimension for overload management at the edge, and pricing is an effective mechanism to exploit it.

cs.DC↗

arXiv2026

WOOTdroid: Whole-system Online On-device Tracing for Android

System auditing on Android faces two problems. First, existing syscall tracers lose events under load, silently overwriting entries faster than a user space reader can drain them. Second, security-relevant application behavior is mediated through Binder, Android's kernel IPC mechanism, and is therefore hidden from the syscall layer. The Binder parcels that the kernel does see carry no method names or typed arguments, a disconnect between low-level events and high-level behavior known as the semantic gap. Existing approaches address the semantic gap either by modifying the Android platform, making them difficult to adjust to OS updates, or by instrumenting the traced application in user space, which sophisticated adversaries can evade by bypassing the instrumented framework APIs. We present WOOTdroid, a design and prototype for on-device tracing on stock Android that addresses both problems without OS modification or application instrumentation. WDSys, an eBPF port of eAudit-style syscall auditing, runs on current Android with at most 3.6% Geekbench overhead and traces 33% more syscalls than ftrace. WDBind captures Binder parcels in the kernel and decodes them out-of-process against a framework signature table extracted via Java reflection. We demonstrate WOOTdroid on Pixel 9 devices running Android 16 with an end-to-end case study reconstructing ten security-relevant Binder transactions.

cs.CR↗

arXiv2025

Diffusion-Based Generation and Imputation of Driving Scenarios from Limited Vehicle CAN Data

Training deep learning methods on small time series datasets that also include corrupted samples is challenging. Diffusion models have shown to be effective to generate realistic and synthetic data, and correct corrupted samples through imputation. In this context, this paper focuses on generating synthetic yet realistic samples of automotive time series data. We show that denoising diffusion probabilistic models (DDPMs) can effectively solve this task by applying them to a challenging vehicle CAN-dataset with long-term data and a limited number of samples. Therefore, we propose a hybrid generative approach that combines autoregressive and non-autoregressive techniques. We evaluate our approach with two recently proposed DDPM architectures for time series generation, for which we propose several improvements. To evaluate the generated samples, we propose three metrics that quantify physical correctness and test track adherence. Our best model is able to outperform even the training data in terms of physical correctness, while showing plausible driving behavior. Finally, we use our best model to successfully impute physically implausible regions in the training data, thereby improving the data quality.

cs.LG↗

arXiv2025

DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understanding

Despite LiDAR (Light Detection and Ranging) being an effective privacy-preserving alternative to RGB cameras to perceive human activities, it remains largely underexplored in the context of multi-modal contrastive pre-training for human activity understanding (e.g., human activity recognition (HAR), retrieval, or person re-identification (RE-ID)). To close this gap, our work explores learning the correspondence between LiDAR point clouds, human skeleton poses, IMU data, and text in a joint embedding space. More specifically, we present DeSPITE, a Deep Skeleton-Pointcloud-IMU-Text Embedding model, which effectively learns a joint embedding space across these four modalities. At the heart of our empirical exploration, we have combined the existing LIPD and Babel datasets, which enabled us to synchronize data of all four modalities, allowing us to explore the learning of a new joint embedding space. Our experiments demonstrate novel human activity understanding tasks for point cloud sequences enabled through DeSPITE, including Skeleton<->Pointcloud<->IMU matching, retrieval, and temporal moment retrieval. Furthermore, we show that DeSPITE is an effective pre-training strategy for point cloud HAR through experiments in MSR-Action3D and HMPEAR.

cs.CV↗

arXiv2025

Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics

Realistic crowd simulations are essential for immersive virtual environments, relying on both individual behaviors (microscopic dynamics) and overall crowd patterns (macroscopic characteristics). While recent data-driven methods like deep reinforcement learning improve microscopic realism, they often overlook critical macroscopic features such as crowd density and flow, which are governed by spatio-temporal spawn dynamics, namely, when and where agents enter a scene. Traditional methods, like random spawn rates, stochastic processes, or fixed schedules, are not guaranteed to capture the underlying complexity or lack diversity and realism. To address this issue, we propose a novel approach called nTPP-GMM that models spatio-temporal spawn dynamics using Neural Temporal Point Processes (nTPPs) that are coupled with a spawn-conditional Gaussian Mixture Model (GMM) for agent spawn and goal positions. We evaluate our approach by orchestrating crowd simulations of three diverse real-world datasets with nTPP-GMM. Our experiments demonstrate the orchestration with nTPP-GMM leads to realistic simulations that reflect real-world crowd scenarios and allow crowd analysis.

cs.LG↗

arXiv2025

SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation

Large Language Models (LLMs) show growing promise in autonomous driving by reasoning over complex traffic scenarios to generate path plans. However, their tendencies toward overconfidence, and hallucinations raise critical safety concerns. We introduce SafePath, a modular framework that augments LLM-based path planning with formal safety guarantees using conformal prediction. SafePath operates in three stages. In the first stage, we use an LLM that generates a set of diverse candidate paths, exploring possible trajectories based on agent behaviors and environmental cues. In the second stage, SafePath filters out high-risk trajectories while guaranteeing that at least one safe option is included with a user-defined probability, through a multiple-choice question-answering formulation that integrates conformal prediction. In the final stage, our approach selects the path with the lowest expected collision risk when uncertainty is low or delegates control to a human when uncertainty is high. We theoretically prove that SafePath guarantees a safe trajectory with a user-defined probability, and we show how its human delegation rate can be tuned to balance autonomy and safety. Extensive experiments on nuScenes and Highway-env show that SafePath reduces planning uncertainty by 77\% and collision rates by up to 70\%, demonstrating effectiveness in making LLM-driven path planning more safer.

cs.LG↗

arXiv2024

LiOn-XA: Unsupervised Domain Adaptation via LiDAR-Only Cross-Modal Adversarial Training

In this paper, we propose LiOn-XA, an unsupervised domain adaptation (UDA) approach that combines LiDAR-Only Cross-Modal (X) learning with Adversarial training for 3D LiDAR point cloud semantic segmentation to bridge the domain gap arising from environmental and sensor setup changes. Unlike existing works that exploit multiple data modalities like point clouds and RGB image data, we address UDA in scenarios where RGB images might not be available and show that two distinct LiDAR data representations can learn from each other for UDA. More specifically, we leverage 3D voxelized point clouds to preserve important geometric structure in combination with 2D projection-based range images that provide information such as object orientations or surfaces. To further align the feature space between both domains, we apply adversarial training using both features and predictions of both 2D and 3D neural networks. Our experiments on 3 real-to-real adaptation scenarios demonstrate the effectiveness of our approach, achieving new state-of-the-art performance when compared to previous uni- and multi-model UDA methods. Our source code is publicly available at https://github.com/JensLe97/lion-xa.

cs.CV↗

arXiv2024

A Tale of Two Scales: Reconciling Horizontal and Vertical Scaling for Inference Serving Systems

Inference serving is of great importance in deploying machine learning models in real-world applications, ensuring efficient processing and quick responses to inference requests. However, managing resources in these systems poses significant challenges, particularly in maintaining performance under varying and unpredictable workloads. Two primary scaling strategies, horizontal and vertical scaling, offer different advantages and limitations. Horizontal scaling adds more instances to handle increased loads but can suffer from cold start issues and increased management complexity. Vertical scaling boosts the capacity of existing instances, allowing for quicker responses but is limited by hardware and model parallelization capabilities. This paper introduces Themis, a system designed to leverage the benefits of both horizontal and vertical scaling in inference serving systems. Themis employs a two-stage autoscaling strategy: initially using in-place vertical scaling to handle workload surges and then switching to horizontal scaling to optimize resource efficiency once the workload stabilizes. The system profiles the processing latency of deep learning models, calculates queuing delays, and employs different dynamic programming algorithms to solve the joint horizontal and vertical scaling problem optimally based on the workload situation. Extensive evaluations with real-world workload traces demonstrate over $10\times$ SLO violation reduction compared to the state-of-the-art horizontal or vertical autoscaling approaches while maintaining resource efficiency when the workload is stable.

cs.DC↗

arXiv2024

NetNN: Neural Intrusion Detection System in Programmable Networks

The rise of deep learning has led to various successful attempts to apply deep neural networks (DNNs) for important networking tasks such as intrusion detection. Yet, running DNNs in the network control plane, as typically done in existing proposals, suffers from high latency that impedes the practicality of such approaches. This paper introduces NetNN, a novel DNN-based intrusion detection system that runs completely in the network data plane to achieve low latency. NetNN adopts raw packet information as input, avoiding complicated feature engineering. NetNN mimics the DNN dataflow execution by mapping DNN parts to a network of programmable switches, executing partial DNN computations on individual switches, and generating packets carrying intermediate execution results between these switches. We implement NetNN in P4 and demonstrate the feasibility of such an approach. Experimental results show that NetNN can improve the intrusion detection accuracy to 99\% while meeting the real-time requirement.

cs.CR↗

arXiv2024

Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling

Mobile and IoT applications increasingly adopt deep learning inference to provide intelligence. Inference requests are typically sent to a cloud infrastructure over a wireless network that is highly variable, leading to the challenge of dynamic Service Level Objectives (SLOs) at the request level. This paper presents Sponge, a novel deep learning inference serving system that maximizes resource efficiency while guaranteeing dynamic SLOs. Sponge achieves its goal by applying in-place vertical scaling, dynamic batching, and request reordering. Specifically, we introduce an Integer Programming formulation to capture the resource allocation problem, providing a mathematical model of the relationship between latency, batch size, and resources. We demonstrate the potential of Sponge through a prototype implementation and preliminary experiments and discuss future works.

cs.DC↗

arXiv2023

The End of the Canonical IoT Botnet: A Measurement Study of Mirai's Descendants

Since the burgeoning days of IoT, Mirai has been established as the canonical IoT botnet. Not long after the public release of its code, researchers found many Mirai variants compete with one another for many of the same vulnerable hosts. Over time, the myriad Mirai variants evolved to incorporate unique vulnerabilities, defenses, and regional concentrations. In this paper, we ask: have Mirai variants evolved to the point that they are fundamentally distinct? We answer this question by measuring two of the most popular Mirai descendants: Hajime and Mozi. To actively scan both botnets simultaneously, we developed a robust measurement infrastructure, BMS, and ran it for more than eight months. The resulting datasets show that these two popular botnets have diverged in their evolutions from their common ancestor in multiple ways: they have virtually no overlapping IP addresses, they exhibit different behavior to network events such as diurnal rate limiting in China, and more. Collectively, our results show that there is no longer one canonical IoT botnet. We discuss the implications of this finding for researchers and practitioners.

cs.CR↗

arXiv2023

Mitigating Intersection Attacks in Anonymous Microblogging

Anonymous microblogging systems are known to be vulnerable to intersection attacks due to network churn. An adversary that monitors all communications can leverage the churn to learn who is publishing what with increasing confidence over time. In this paper, we propose a protocol for mitigating intersection attacks in anonymous microblogging systems by grouping users into anonymity sets based on similarities in their publishing behavior. The protocol provides a configurable communication schedule for users in each set to manage the inevitable trade-off between latency and bandwidth overhead. In our evaluation, we use real-world datasets from two popular microblogging platforms, Twitter and Reddit, to simulate user publishing behavior. The results demonstrate that the protocol can protect users against intersection attacks at low bandwidth overhead when the users adhere to communication schedules. In addition, the protocol can sustain a slow degradation in the size of the anonymity set over time under various churn rates.

cs.CR↗

arXiv2023

Reconciling High Accuracy, Cost-Efficiency, and Low Latency of Inference Serving Systems

The use of machine learning (ML) inference for various applications is growing drastically. ML inference services engage with users directly, requiring fast and accurate responses. Moreover, these services face dynamic workloads of requests, imposing changes in their computing resources. Failing to right-size computing resources results in either latency service level objectives (SLOs) violations or wasted computing resources. Adapting to dynamic workloads considering all the pillars of accuracy, latency, and resource cost is challenging. In response to these challenges, we propose InfAdapter, which proactively selects a set of ML model variants with their resource allocations to meet latency SLO while maximizing an objective function composed of accuracy and cost. InfAdapter decreases SLO violation and costs up to 65% and 33%, respectively, compared to a popular industry autoscaler (Kubernetes Vertical Pod Autoscaler).

cs.LG↗

arXiv2023

Understanding Stationary and Moving Direct Skin Vibrotactile Stimulation on the Palm

Palm-based tactile displays have the potential to evolve from single motor interfaces (e.g., smartphones) to high-resolution tactile displays (e.g., back-of-device haptic interfaces) enabling richer multi-modal experiences with more information. However, we lack a systematic understanding of vibrotactile perception on the palm and the influence of various factors on the core design decisions of tactile displays (number of actuators, resolution, and intensity). In a first experiment (N=16), we investigated the effect of these factors on the users' ability to localize stationary sensations. In a second experiment (N=20), we explored the influence of resolution on recognition rate for moving tactile sensations.Findings show that for stationary sensations a 9 actuator display offers a good trade-off and a $3\times3$ resolution can be accurately localized. For moving sensations, a $2\times4$ resolution led to the highest recognition accuracy, while $5\times10$ enables higher resolution output with a reasonable accuracy.

cs.HC↗

arXiv2023

Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series

In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved. Deep learning-based state-of-the-art methods for LiDAR MOS strongly depend on annotated ground truth data, which is expensive to obtain and scarce in existence. To close this gap in the stationary setting, we propose a novel 4D LiDAR representation based on multivariate time series that relaxes the problem of unsupervised MOS to a time series clustering problem. More specifically, we propose modeling the change in occupancy of a voxel by a multivariate occupancy time series (MOTS), which captures spatio-temporal occupancy changes on the voxel level and its surrounding neighborhood. To perform unsupervised MOS, we train a neural network in a self-supervised manner to encode MOTS into voxel-level feature representations, which can be partitioned by a clustering algorithm into moving or stationary. Experiments on stationary scenes from the Raw KITTI dataset show that our fully unsupervised approach achieves performance that is comparable to that of supervised state-of-the-art approaches.

cs.CV↗

arXiv2023

Unsupervised Driving Event Discovery Based on Vehicle CAN-data

The data collected from a vehicle's Controller Area Network (CAN) can quickly exceed human analysis or annotation capabilities when considering fleets of vehicles, which stresses the importance of unsupervised machine learning methods. This work presents a simultaneous clustering and segmentation approach for vehicle CAN-data that identifies common driving events in an unsupervised manner. The approach builds on self-supervised learning (SSL) for multivariate time series to distinguish different driving events in the learned latent space. We evaluate our approach with a dataset of real Tesla Model 3 vehicle CAN-data and a two-hour driving session that we annotated with different driving events. With our approach, we evaluate the applicability of recent time series-related contrastive and generative SSL techniques to learn representations that distinguish driving events. Compared to state-of-the-art (SOTA) generative SSL methods for driving event discovery, we find that contrastive learning approaches reach similar performance.

cs.LG↗

arXiv2022

User-Level Label Leakage from Gradients in Federated Learning

Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the common belief that this provides privacy benefits, we here add to the very recent results on privacy risks when sharing gradients. Specifically, we investigate Label Leakage from Gradients (LLG), a novel attack to extract the labels of the users' training data from their shared gradients. The attack exploits the direction and magnitude of gradients to determine the presence or absence of any label. LLG is simple yet effective, capable of leaking potential sensitive information represented by labels, and scales well to arbitrary batch sizes and multiple classes. We mathematically and empirically demonstrate the validity of the attack under different settings. Moreover, empirical results show that LLG successfully extracts labels with high accuracy at the early stages of model training. We also discuss different defense mechanisms against such leakage. Our findings suggest that gradient compression is a practical technique to mitigate the attack.

cs.CR↗