TY - GEN A1 - Schuster, Franka A1 - Larisch, René T1 - KI-basierte Angriffserkennung: Von der Blackbox zum verständlichen System T2 - 54. Kraftwerkstechni­sches Kolloquium 2022 Y1 - 2022 UR - https://tu-dresden.de/ing/maschinenwesen/ifvu/evt/kraftwerkstechnisches-kolloquium/54-kraftwerkstechnisches-kolloquium-2022-1 ER - TY - GEN A1 - Pennekamp, Jan A1 - Henze, Martin A1 - Zinnen, Andreas A1 - Lanze, Fabian A1 - Wehrle, Klaus A1 - Panchenko, Andriy T1 - CUMUL & Co: High-Impact Artifacts for Website Fingerprinting Research T2 - 38th Annual Computer Security Applications Conference (ACSAC 2022), Austin, Texas, USA, December 5-9, 2022. N2 - Anonymous communication on the Internet is about hiding the relationship between communicating parties. At NDSS ’16, we presented a new website fingerprinting approach, CUMUL, that utilizes novel features and a simple yet powerful algorithm to attack anonymization networks such as Tor. Based on pattern observation of data flows, this attack aims at identifying the content of encrypted and anonymized connections. Apart from the feature generation and the used classifier, we also provided a large dataset to the research community to study the attack at Internet scale. In this paper, we emphasize the impact of our artifacts by analyzing publications referring to our work with respect to the dataset, feature extraction method, and source code of the implementation. Based on this data, we draw conclusions about the impact of our artifacts on the research field and discuss their influence on related cybersecurity topics. Overall, from 393 unique citations, we discover more than 130 academic references that utilize our artifacts, 61 among them are highly influential (according to SemanticScholar), and at least 43 are from top-ranked security venues. This data underlines the significant relevance and impact of our work as well as of our artifacts in the community and beyond. KW - Traffic Analysis;Website Fingerprinting; Privacy; Anonymous Communication; Onion Routing; Web Privacy Y1 - 2022 UR - https://www.acsac.org/2022/program/artifacts_competition/ ER - TY - GEN A1 - Reuter, Sebastian A1 - Hiller, Jens A1 - Pennekamp, Jan A1 - Panchenko, Andriy A1 - Wehrle, Klaus T1 - Demo: Traffic Splitting for Tor – A Defense against Fingerprinting Attacks T2 - Conference on Networked Systems 2021 (NetSys 2021) N2 - Website fingerprinting (WFP) attacks on the anonymity network Tor have become ever more effective. Furthermore, research discovered that proposed defenses are insufficient or cause high overhead. In previous work, we presented a new WFP defense for Tor that incorporates multipath transmissions to repel malicious Tor nodes from conducting WFP attacks. In this demo, we showcase the operation of our traffic splitting defense by visually illustrating the underlying Tor multipath transmission using LED-equipped Raspberry Pis. KW - Onion Routing; Website Fingerprinting; Multipath Traffic; Privacy Y1 - 2021 U6 - https://doi.org/10.14279/tuj.eceasst.80.1151.1128 SN - 1863-2122 ER - TY - GEN A1 - Gao, Yunlong A1 - Zhang, Yisong A1 - Pan, Jinyan A1 - Luo, Sizhe A1 - Yang, Chengyu T1 - Discriminant analysis based on reliability of local neighborhood T2 - Expert Systems with Applications N2 - To obtain a compact and effective low-dimensional representation, recently, most existing discriminant manifold learning methods have integrated manifold learning into discriminant analysis (DA) for extracting the intrinsic structure of data. These methods learn two kinds of adjacency graphs, such as intrinsic graph and penalty graph, to characterize the similarity between samples from intraclass and the pseudo similarity of interclass. However, they treat every sample equally, which results in the following defects: (1) These methods cannot accurately characterize the marginal region among different classes only through penalty graphs. (2) They can not identify the noisy and outlier samples which reduce the robustness of these methods. To address these problems, we introduce an adaptive adjacency factor to perform the discriminative based reliability analysis for each sample. By integrating the adjacency factor into discriminant manifold learning methods, we propose a novel method for DA namely discriminant analysis based on reliability of local neighborhood (DA-RoLN). We mainly have three contributions in this paper: (1) By the introduction of adjacency factor, sample points can be divided into three parts: intraclass samples, marginal samples, and outliers. Therefore, DA-RoLN emphasizes the effect of valid samples and filters the influence of outliers. (2) We adaptively calculate the adjacency factor in low-dimensional space, thus, the margin between different classes in low-dimensional space is emphasized. (3) An iterative algorithm is developed to solve the objective function of DA-RoLN, and it is easy to solve with a low computational cost. Extensive experimental results show the effectiveness of DA-RoLN. KW - Dimensionality reduction KW - Discriminant analysis KW - Manifold learning KW - Graph learning KW - Adjacency factor Y1 - 2021 U6 - https://doi.org/10.1016/j.eswa.2021.114790 SN - 1873-6793 SN - 0957-4174 VL - 175 ER - TY - GEN A1 - Yi, Chao A1 - Wu, Shunxiang A1 - Xi, Bin A1 - Ming, Daodong A1 - Zhang, Yisong A1 - Zhou, Zhenwen T1 - Terrorist Video Detection System Based on Faster R-CNN and LightGBM T2 - CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering N2 - Nowadays the mobile phone has become an indispensable tool in the lives of many people. While facilitating people's lives, it also provides criminals with a very important tool for spreading the terrorist video. Traditional manual detection of the terrorist video has the problem of low accuracy and inefficiency. To address the issue, this paper proposes a terrorist video detection system based on Light Gradient Boosting Machine (LightGBM) and Faster Region-based Convolutional Neural Network (Faster R-CNN) for mobile phone forensics system, which is used to quickly detect whether there is a terrorist video in the suspect's mobile phone. The system uses a multi-model method for detection, which includes preliminary detection and deep detection in two stages. Experimental research shows that it can effectively and accurately detect terrorist videos in mobile phones, thereby helping criminal investigation personnel to quickly grasp criminal evidence and provide some clues for the detection of the case. Y1 - 2020 UR - https://dl.acm.org/doi/abs/10.1145/3424978.3425121 SN - 978-1-4503-7772-0 U6 - https://doi.org/10.1145/3424978.3425121 SP - 1 EP - 8 ER - TY - GEN A1 - Gao, Yunlong A1 - Luo, Si-Zhe A1 - Pan, Jin-Yan A1 - Chen, Bai-Hua A1 - Zhang, Yi-Song T1 - Robust PCA Using Adaptive Probability Weighting T2 - Acta Automatica Sinica N2 - Principal component analysis (PCA) is an important method for processing high-dimensional data. In recent years, PCA models based on various norms have been extensively studied to improve the robustness. However, on the one hand, these algorithms do not consider the relationship between reconstruction error and covariance; on the other hand, they lack the uncertainty of considering the principal component to the data description. Aiming at these problems, this paper proposes a new robust PCA algorithm. Firstly, the L2,p-norm is used to measure the reconstruction error and the description variance of the projection data. Based on the reconstruction error and the description variance, the adaptive probability error minimization model is established to calculate the uncertainty of the principal component's description of the data. Based on the uncertainty, the adaptive probability weighting PCA is established. The corresponding optimization method is designed. The experimental results of artificial data sets, UCI data sets and face databases show that RPCA-PW is superior than other PCA algorithms. KW - Principle component analysis (PCA) KW - weighted principal component analysis (WPCA) KW - dimensionality reduction KW - robustness Y1 - 2021 U6 - https://doi.org/10.16383/j.aas.c180743 SN - 0254-4156 VL - 47 IS - 4 SP - 825 EP - 838 ER - TY - GEN A1 - Gao, Yunlong A1 - Lin, Tingting A1 - Zhang, Yisong A1 - Luo, Sizhe A1 - Nie, Feiping T1 - Robust principal component analysis based on discriminant information T2 - IEEE Transactions on Knowledge and Data Engineering N2 - Recently, several robust principal component analysis (RPCA) models were presented to enhance the robustness of PCA by exploiting the robust norms as their loss functions. But an important problem is that they have no ability to discriminate outliers from correct samples. To solve this problem, we propose a robust principal component analysis based on discriminant information (RPCA-DI). RPCA-DI disentangles the robust PCA with a two-step fashion: the identification and the processing of outliers. To identity outliers, a sample representation model based on entropy regularization is constructed to analyze the membership of data belonging to the principal component space(PC) and its orthogonal complement(OC), the discriminative information of data will be extracted based on measuring the differences of retained information on PC(or OC) of data. By this way, we can discriminate correct samples when we deal with outliers, which is more reasonable for robustness learning respective to previous works. In the noise processing step, in addition to considering the levels of noise, the resistance of the sample points to noise is also considered to prevent overfitting, thereby improving the generalization performance of RPCA-DI. Finally, an iterative algorithm is designed to solve the corresponding model. Compared with some state-of-art RPCA methods on artificial datasets, UCI datasets and face databases that verifies the effectiveness of our proposed algorithm. KW - Principal component analysis KW - entropy regularization KW - robustness Y1 - 2023 U6 - https://doi.org/10.1109/TKDE.2021.3093447 SN - 1558-2191 SN - 1041-4347 VL - 35 IS - 2 SP - 1991 EP - 2003 PB - IEEE ER - TY - GEN A1 - Berti, Alessandro A1 - Koren, Istvan A1 - Adams, Jan Niklas A1 - Park, Gyunam A1 - Knopp, Benedikt A1 - Graves, Nina A1 - Rafiei, Majid A1 - Liß, Lukas A1 - Tacke Genannt Unterberg, Leah A1 - Zhang, Yisong A1 - Schwanen, Christopher A1 - Pegoraro, Marco A1 - Aalst, Wil van der T1 - OCEL (Object-Centric Event Log) 2.0 Specification T2 - arXiv N2 - Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM techniques. OCEL 2.0 forms the new, more expressive standard, allowing for more extensive process analyses while remaining in an easily exchangeable format. In contrast to the first OCEL standard, it can depict changes in objects, provide information on object relationships, and qualify these relationships to other objects or specific events. Compared to XES, it is more expressive, less complicated, and better readable. OCEL 2.0 offers three exchange formats: a relational database (SQLite), XML, and JSON format. This OCEL 2.0 specification document provides an introduction to the standard, its metamodel, and its exchange formats, aimed at practitioners and researchers alike. Y1 - 2024 UR - https://arxiv.org/abs/2403.01975 SP - 1 EP - 49 ER - TY - GEN A1 - Zhang, Yisong A1 - Aalst, Wil van der T1 - Explorative Process Discovery Using Activity Projections T2 - International Conference on Applications and Theory of Petri Nets and Concurrency, PETRI NETS 2023 N2 - This paper presents a tool to Explore Process Discovery (EPD) results using activity projection. Our EPD-Tool aims at exploring quality changes after removing activities from an event log. The main idea is to create a projected event log for every non-empty subset of activities and apply process discovery and conformance checking on them. The tool has been implemented as a plugin in ProM. First, EPD-Tool uses a process discovery algorithm to discover Petri net models for each projected event log. Then, EPD-Tool uses a conformance checking technique to compute conformance measures for each projected event log and model pair (L, N), e.g., fitness, precision, and F1-score. Finally, a dendrogram is generated to visualize the relationship between each log-model pair, thus enabling the systematic exploration of the different models using the dendrogram to find the best-performing node, i.e., a best log-model pair. This method prioritizes activities and detects redundancy in the process, which contributes to process enhancement. Conversely, critical activities are uncovered to help to shorten the processing time or save the process cost. This paper presents the EPD-Tool implementation and some example results. KW - Process mining KW - Petri nets KW - Log projection KW - ProM Y1 - 2023 SN - 978-3-031-33619-5 SN - 978-3-031-33620-1 U6 - https://doi.org/10.1007/978-3-031-33620-1_13 SP - 229 EP - 239 PB - Springer, Cham ER - TY - GEN A1 - Giustolisi, Rosario A1 - Sheikhi‐Garjan, Maryam A1 - Schuermann, Carsten T1 - Thwarting Last-Minute Voter Coercion T2 - 45th IEEE Symposium on Security and Privacy Sponsored by the IEEE Computer Society Technical Committee on Security and Privacy in cooperation with the International Association for Cryptologic Research Y1 - 2024 UR - https://eprint.iacr.org/2023/1876 ER - TY - GEN A1 - Sheikhi‐Garjan, Maryam A1 - Orhon Kılıç, N. Gamze A1 - Cenk, Murat T1 - Supersingular Isogeny-based Ring Signature T2 - International Journal of Information Security Science N2 - The increasing demand for secure and anonymous transactions raises the popularity of ring signatures, which is a digital signature scheme that allows identifying a group of possible signers without revealing the identity of the actual signer. This paper presents efficient supersingular isogeny-based ring signature and linkable ring signature schemes that will find potential applications in post-quantum technologies. We develop the ring signature scheme by applying the Fiat-Shamir transform on the sigma protocol for a ring which we obtain from the supersingular isogeny-based interactive zero-knowledge identification scheme by adopting the scheme for a ring. We also extend our ring signature protocol with an additional parameter, i.e., a tag that provides to detect if a signer issues two signatures concerning the same ring by preserving anonymity and linkable anonymity. The signature size of our ring signature protocols increases logarithmically in the size of the ring thanks to the Merkle trees. We show the security proofs and efficiency analyses of the protocols offered. Moreover, we provide the implementation results of the supersingular isogeny-based ring signature, which offers small signature sizes for NIST post-quantum security levels. Y1 - 2023 U6 - https://doi.org/10.55859/ijiss.1187756 SN - 2147-0030 VL - 12 IS - 1 SP - 32 EP - 57 PB - Seref Sagiroglu ER -