arXiv subjects

Wenmao Liu

Publications and source records attributed to Wenmao Liu.

4 recordsLinked to original sources

arXiv2025

Adversarial Example Based Fingerprinting for Robust Copyright Protection in Split Learning

Currently, deep learning models are easily exposed to data leakage risks. As a distributed model, Split Learning thus emerged as a solution to address this issue. The model is splitted to avoid data uploading to the server and reduce computing requirements while ensuring data privacy and security. However, the transmission of data between clients and server creates a potential vulnerability. In particular, model is vulnerable to intellectual property (IP) infringement such as piracy. Alarmingly, a dedicated copyright protection framework tailored for Split Learning models is still lacking. To this end, we propose the first copyright protection scheme for Split Learning model, leveraging fingerprint to ensure effective and robust copyright protection. The proposed method first generates a set of specifically designed adversarial examples. Then, we select those examples that would induce misclassifications to form the fingerprint set. These adversarial examples are embedded as fingerprints into the model during the training process. Exhaustive experiments highlight the effectiveness of the scheme. This is demonstrated by a remarkable fingerprint verification success rate (FVSR) of 100% on MNIST, 98% on CIFAR-10, and 100% on ImageNet, respectively. Meanwhile, the model's accuracy only decreases slightly, indicating that the embedded fingerprints do not compromise model performance. Even under label inference attack, our approach consistently achieves a high fingerprint verification success rate that ensures robust verification.

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arXiv2024

Marlin: Knowledge-Driven Analysis of Provenance Graphs for Efficient and Robust Detection of Cyber Attacks

Recent research in both academia and industry has validated the effectiveness of provenance graph-based detection for advanced cyber attack detection and investigation. However, analyzing large-scale provenance graphs often results in substantial overhead. To improve performance, existing detection systems implement various optimization strategies. Yet, as several recent studies suggest, these strategies could lose necessary context information and be vulnerable to evasions. Designing a detection system that is efficient and robust against adversarial attacks is an open problem. We introduce Marlin, which approaches cyber attack detection through real-time provenance graph alignment.By leveraging query graphs embedded with attack knowledge, Marlin can efficiently identify entities and events within provenance graphs, embedding targeted analysis and significantly narrowing the search space. Moreover, we incorporate our graph alignment algorithm into a tag propagation-based schema to eliminate the need for storing and reprocessing raw logs. This design significantly reduces in-memory storage requirements and minimizes data processing overhead. As a result, it enables real-time graph alignment while preserving essential context information, thereby enhancing the robustness of cyber attack detection. Moreover, Marlin allows analysts to customize attack query graphs flexibly to detect extended attacks and provide interpretable detection results. We conduct experimental evaluations on two large-scale public datasets containing 257.42 GB of logs and 12 query graphs of varying sizes, covering multiple attack techniques and scenarios. The results show that Marlin can process 137K events per second while accurately identifying 120 subgraphs with 31 confirmed attacks, along with only 1 false positive, demonstrating its efficiency and accuracy in handling massive data.

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arXiv2023

Imperceptible Sample-Specific Backdoor to DNN with Denoising Autoencoder

The backdoor attack poses a new security threat to deep neural networks. Existing backdoor often relies on visible universal trigger to make the backdoored model malfunction, which are not only usually visually suspicious to human but also catchable by mainstream countermeasures. We propose an imperceptible sample-specific backdoor that the trigger varies from sample to sample and invisible. Our trigger generation is automated through a desnoising autoencoder that is fed with delicate but pervasive features (i.e., edge patterns per images). We extensively experiment our backdoor attack on ImageNet and MS-Celeb-1M, which demonstrates stable and nearly 100% (i.e., 99.8%) attack success rate with negligible impact on the clean data accuracy of the infected model. The denoising autoeconder based trigger generator is reusable or transferable across tasks (e.g., from ImageNet to MS-Celeb-1M), whilst the trigger has high exclusiveness (i.e., a trigger generated for one sample is not applicable to another sample). Besides, our proposed backdoored model has achieved high evasiveness against mainstream backdoor defenses such as Neural Cleanse, STRIP, SentiNet and Fine-Pruning.

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arXiv2022

BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis

Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications. This is partly due to the transparency associated with the underpinning ledgers, where any individual can access the record of a transaction record on the public ledger. In this paper, we build a dataset comprising Bitcoin transactions between 12 July 2019 and 26 May 2021. This dataset (hereafter referred to as BABD-13) contains 13 types of Bitcoin addresses, 5 categories of indicators with 148 features, and 544,462 labeled data, which is the largest labeled Bitcoin address behavior dataset publicly available to our knowledge. We then use our proposed dataset on common machine learning models, namely: k-nearest neighbors algorithm, decision tree, random forest, multilayer perceptron, and XGBoost. The results show that the accuracy rates of these machine learning models for the multi-classification task on our proposed dataset are between 93.24% and 97.13%. We also analyze the proposed features and their relationships from the experiments, and propose a k-hop subgraph generation algorithm to extract a k-hop subgraph from the entire Bitcoin transaction graph constructed by the directed heterogeneous multigraph starting from a specific Bitcoin address node (e.g., a known transaction associated with a criminal investigation). Besides, we initially analyze the behavior patterns of different types of Bitcoin addresses according to the extracted features.

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