Yajie Wang

Online service systems operate under high concurrency, dynamic workloads, and distributed execution environments, which increase their susceptibility to faults and performance degradation. To ensure stable operation, these systems commonly monitor performance metrics, where timely anomaly detection enables early intervention and fault prevention. Existing metrics-based anomaly detection methods are typically unsupervised, reconstructing unlabeled data to model normal patterns and detecting anomalies as deviations from these patterns. However, they often overlook the presence of unlabeled anomalous data (noise) within the training set, which hinders the model's ability to accurately learn normal behavior and degrades detection accuracy. To address this limitation, we propose a noise-robust, representation-enhanced reconstruction-based anomaly detection method for metrics. The method trains a reconstruction model to capture normal time-series patterns and identifies noisy samples based on their deviation from the latent representation center, assigning them lower training weights. Both the reconstruction outputs and the representation centers jointly define the normal pattern of each sample, reducing the influence of anomalous noise during modeling. Extensive experiments show that our method achieves an F1 score of 97.00% on four widely used datasets, surpassing existing state-of-the-art approaches. Ablation studies further confirm the effectiveness of each module.

To boost the encoder stealing attack under the perturbation-based defense that hinders the attack performance, we propose a boosting encoder stealing attack with perturbation recovery named BESA. It aims to overcome perturbation-based defenses. The core of BESA consists of two modules: perturbation detection and perturbation recovery, which can be combined with canonical encoder stealing attacks. The perturbation detection module utilizes the feature vectors obtained from the target encoder to infer the defense mechanism employed by the service provider. Once the defense mechanism is detected, the perturbation recovery module leverages the well-designed generative model to restore a clean feature vector from the perturbed one. Through extensive evaluations based on various datasets, we demonstrate that BESA significantly enhances the surrogate encoder accuracy of existing encoder stealing attacks by up to 24.63% when facing state-of-the-art defenses and combinations of multiple defenses.

Vision Transformers (ViTs) have emerged as a dominant backbone architecture for a variety of visual tasks; however, their vulnerability to adversarial examples continues to pose a significant challenge. Unlike Convolutional Neural Networks (CNNs), ViTs fundamentally rely on self-attention mechanisms, leading to a distinct architectural design. The limited transferability of existing adversarial attacks on ViTs can be attributed to the neglect of these unique features. To address this, we introduce a novel self-attention-oriented Adversarial Block Skip (ABS) method specifically designed to generate transferable adversarial examples. ABS aims to create a diverse range of structures by applying skip connections to blocks within the transformer encoder, thereby activating the uncertainty of the attention mechanism. This disrupts the global interaction between different features captured by ViTs, thereby confounding the model's decision-making process. The results unequivocally demonstrate that the ABS not only establishes a versatile and efficacious attack mechanism but also supports transfer attacks across a diverse array of ViTs and CNNs. This finding emphasizes the significant generalization capabilities of ViTs within the adversarial landscape, suggesting that their resilience and adaptability under such conditions may surpass previous assumptions. Comprehensive empirical evaluations involving various prominent transformer models on the ImageNet dataset substantiate that ABS markedly surpasses existing baseline methods in terms of effectiveness. Furthermore, ABS is highly compatible with prevailing adversarial attack frameworks, augmenting their efficacy upon integration. Such versatility renders ABS an indispensable component of the toolkit for executing advanced and effective adversarial attacks in the realm of machine learning security.

Federated learning (FL) has gained significant traction as a paradigm for decentralized learning, enabling multiple clients to collaboratively train models without sharing their local data. However, applying FL to graph-structured data introduces unique challenges, such as handling non-IID data and preserving the structural dependencies between nodes. Additionally, existing approaches to FL with graph neural networks (GNNs) often struggle to capture complex relationships within graph data and are vulnerable to privacy breaches, including membership inference and model inversion attacks. In this article, we propose federated capsule graph neural networks (FCGNNs), a novel architecture that integrates the dynamic routing capabilities of capsule networks with the structure-preserving power of GNNs in a federated setting. FCGNN is designed to effectively model hierarchical and part-whole relationships within graph data, enabling it to outperform traditional Federated GNN approaches. We enhance the privacy of FCGNN by incorporating differential privacy and secure aggregation techniques, ensuring that individual client updates remain confidential while maintaining strong model performance. We evaluate FCGNN on several benchmark graph datasets, including Cora, Citeseer, PubMed, and PROTEINS, and demonstrate that it consistently achieves higher accuracy and F1-scores compared to existing FL methods. Our experiments show that FCGNN converges faster and incurs lower communication costs, making it highly efficient for real-world FL applications. Furthermore, FCGNN is robust across different numbers of participating clients, maintaining high performance even in non-IID scenarios. These results highlight the potential of FCGNN as a scalable and privacy-preserving solution for decentralized learning on graph-structured data.