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PublicationTowards a Better Understanding of Machine Learning based Network Intrusion Detection Systems in Industrial Networks( 2022)It is crucial in an industrial network to understand how and why a intrusion detection system detects, classifies, and reports intrusions. With the ongoing introduction of machine learning into the research area of intrusion detection, this understanding gets even more important since the used systems often appear as a black-box for the user and are no longer understandable in an intuitive and comprehensible way. We propose a novel approach to understand the internal characteristics of a machine learning based network intrusion detection system. This approach includes methods to understand which data sources the system uses, to evaluate whether the system uses linear or non-linear classification approaches, and to find out which underlying machine learning model is implemented in the system. Our evaluation on two publicly available industrial datasets shows that the detection of the data source and the differentiation between linear and non-linear models is possible with our approach. In addition, the identification of the underlying machine learning model can be accomplished with statistical significance for non-linear models. The information made accessible by our approach helps to develop a deeper understanding of the functioning of a network intrusion detection system, and contributes towards developing transparent machine learning based intrusion detection approaches.
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PublicationCluster Crash: Learning from Recent Vulnerabilities in Communication Stacks( 2022)To ensure functionality and security of network stacks in Industrial Devices, thorough testing is necessary. This includes blackbox network fuzzing, where fields in network packets are filled with unexpected values to test the device's behavior in edge cases. Due to resource constraints, the tests need to be efficient and such the input values need to be chosen intelligently. Previous solutions use heuristics based on vague knowledge from previous projects to make these decisions. We aim to structure existing knowledge by defining Vulnerability Anti-Patterns for network communication stacks based on an analysis of the recent vulnerability groups Ripple20, Amnesia:33, and Urgent/11. For our evaluation, we implement fuzzing test scripts based on the Vulnerability Anti-Patterns and run them against 8 Industrial Devices from 5 different device classes. We show (I) that similar vulnerabilities occur in implementations of the same protocol as well as in different protocols, (II) that similar vulnerabilities also spread over different device classes, and (III) that test scripts based on the Vulnerability Anti-Patterns help to identify these vulnerabilities.
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PublicationComposition and Symmetries - Computational Analysis of Fine-Art Aesthetics( 2022)This article deals with the problem of quantitative research of the aesthetic content of the fine-art object. The paper states that a fine-art object is a conceptually formed sequence of signs, and its composition is a structural form, that can be measured using mathematical models. The main approach is based on the perception of the formal order as a determinant of the aesthetic category of beauty. The composition of the image is directly related to the formation of aesthetic sensations and values, since it performs the function of controlling the viewer's perception of a work of art. The research is based on the studies of computational aesthetics by G. D. Birkhoff and M. Bense, as well as the studies of the receptive aesthetics of R. Ingarden, W. Iser, H. R. Jauss and Ya. Mukarzhovsky. The computational aesthetics methods, such as CNN-based object detectors, and gestalt-based symmetry analysis, are used to detect symmetry axes in fine-art images. Experimental analysis demonstrates that the applied computational approach is consistent with the philosophical analysis and the expert evaluations of the fine-art images, therefore it allows to obtain more detailed fine-art paintings description.
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PublicationImproving Semantic Image Segmentation via Label Fusion in Semantically Textured Meshes( 2022)Models for semantic segmentation require a large amount of hand-labeled training data which is costly and time-consuming to produce. For this purpose, we present a label fusion framework that is capable of improving semantic pixel labels of video sequences in an unsupervised manner. We make use of a 3D mesh representation of the environment and fuse the predictions of different frames into a consistent representation using semantic mesh textures. Rendering the semantic mesh using the original intrinsic and extrinsic camera parameters yields a set of improved semantic segmentation images. Due to our optimized CUDA implementation, we are able to exploit the entire c-dimensional probability distribution of annotations over c classes in an uncertainty-aware manner. We evaluate our method on the Scannet dataset where we improve annotations produced by the state-of-the-art segmentation network ESANet from 52.05% to 58.25% pixel accuracy. We publish the source code of our framework online to foster future research in this area (https://github.com/fferflo/semantic-meshes). To the best of our knowledge, this is the first publicly available label fusion framework for semantic image segmentation based on meshes with semantic textures.
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PublicationFast and Lightweight Online Person Search for Large-Scale Surveillance Systems( 2022)The demand for methods for video analysis in the fieldof surveillance technology is rapidly growing due to theincreasing amount of surveillance footage available. Intelligent methods for surveillance software offer numerouspossibilities to support police investigations and crime prevention. This includes the integration of video processingpipelines for tasks such as detection of graffiti, suspiciousluggage, or intruders. Another important surveillance taskis the semi-automated search for specific persons-of-interestwithin a camera network. In this work, we identify the major obstacles for the development of person search systemsas the real-time processing capability on affordable hardware and the performance gap of person detection and reidentification methods on unseen target domain data. Inaddition, we demonstrate the current potential of intelligentonline person search by developing a real-world, largescale surveillance system. An extensive evaluation is provided for person detection, tracking, and re-identificationcomponents on affordable hardware setups, for which thewhole system achieves real-time processing up to 76 FPS.
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PublicationWhere are we with Human Pose Estimation in Real-World Surveillance?( 2022)The rapidly increasing number of surveillance cameras offers a variety of opportunities for intelligent video analytics to improve public safety. Among many others, the automatic recognition of suspicious and violent behavior poses a key task. To preserve personal privacy, prevent ethnic bias, and reduce complexity, most approaches first extract the pose or skeleton of persons and subsequently perform activity recognition. However, current literature mainly focuses on research datasets and does not consider real-world challenges and requirements of human pose estimation. We close this gap by analyzing these challenges, such as inadequate data and the need for real-time processing, and proposing a framework for human pose estimation in uncontrolled crowded surveillance scenarios. Our system integrates mitigation measures as well as a tracking component to incorporate temporal information. Finally, we provide a detailed quantitative and qualitative analysis on both a scientific and a real-world dataset to highlight improvements and remaining obstacles towards robust real-world human pose estimation in uncooperative scenarios.
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PublicationModelling Ambiguous Assignments for Multi-Person Tracking in Crowds( 2022)Multi-person tracking is often solved with a tracking-by-detection approach that matches all tracks and detections simultaneously based on a distance matrix. In crowded scenes, ambiguous situations with similar track-detection distances occur, which leads to wrong assignments. To mitigate this problem, we propose a new association method that separately treats such difficult situations by modelling ambiguous assignments based on the differences in the distance matrix. Depending on the numbers of tracks and detections, for which the assignment task is determined ambiguous, different strategies to resolve these ambiguous situations are proposed. To further enhance the performance of our tracking framework, we introduce a camera motion-aware interpolation technique and make an adaptation to the motion model, which improves identity preservation. The effectiveness of our approach is demonstrated through extensive ablative experiments with different detection models. Moreover, the superiority w.r.t. other trackers is shown on the challenging MOT17 and MOT20 datasets, where state-of-the-art results are obtained.
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PublicationThe Design and Implementation of ZEUS: Novel Support in Managing Large-Scale Evacuations( 2021)
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PublicationNovel highly efficient in-band pump wavelengths for Ho-doped fiber amplifiers( 2021)We report the design and performance of Holmium-doped fiber amplifiers (HDFAs) with novel alternative in-band pump wavelengths in the 1720-2000 nm spectral region. We demonstrate through simulations that pump wavelengths of 1840-1860 nm can yield significantly improved output power (3-6dB), gain (8-10 dB) , and optical-optical conversion efficiency compared to the previous technical and industry standard pump wavelength of 1940 nm. Experimental results fully confirm our simulations.
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PublicationInternational Data Spaces tailored to Industrie 4.0 - How to Address the Requirements for Data Sovereignty( 2021)This paper describes the working approach of the IDS-Industrial Community (IDS-I) for the analysis of requirements on data sovereignty. This activity is motivated by the vision 2030 of the Platform Industrie 4.0 that states autonomy, including data sovereignty, as one strategic field of action. The paper presents how IDS-I aims at systematically deriving data sovereignty aspects from the two reference use cases, Collaborative Condition Monitoring (CCM) and Smart Factory Web (SFW), in order to identify architectural and technological synergies and gaps between the International Data Spaces (IDS) and the specifications of the Platform Industrie 4.0
