Once a very large payment processor client told us: 'if we are down for 5 minutes, we open the evening news - so don't screw up'. Processing billions of dollars per day, many financial institutions, need to continuously fight organized crime in the form ...

Hyperbolic geometry have shown significant potential in modeling complex structured data, particularly those with underlying tree-like and hierarchical structures. Despite the impressive performance of various hyperbolic neural networks across numerous ...

In the Bitcoin blockchain, an entity (e.g., a gambling service) may control multiple distinct address clusters. Links (i.e., trust relationships) between these disjoint address clusters can be established when one cluster is abandoned, and a new one is ...

The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studies have concentrated on privacy leakage via public user attributes, the ...

Prior research on neural architecture search (NAS) for adversarial robustness has revealed that a lightweight and adversarially robust sub-network could exist in a non-robust large teacher network. Such a sub-network is generally discovered based on ...

The deployment of learning-based models to detect malicious activities in network traffic flows is significantly challenged by concept drift. With evolving attack technology and dynamic attack behaviors, the underlying data distribution of recently ...

Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Despite demonstrating comparable performance with supervised methods,...

Preserving individual privacy while enabling collaborative data sharing is crucial for organizations. Synthetic data generation is one solution, producing artificial data that mirrors the statistical properties of private data. While numerous techniques ...

Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learning. While GPL offers superior effectiveness in graph knowledge transfer ...

The popularity of decentralized finance has drawn attention to liquidity mining (LM). In LM, a user deposits her cryptocurrencies into liquidity pools to provide liquidity for exchanges and earn yields. Different liquidity pools offer varying yields and ...

Graph Neural Networks (GNNs) have shown remarkable performance in various tasks. However, recent works reveal that GNNs are vulnerable to backdoor attacks. Generally, backdoor attack poisons the graph by attaching backdoor triggers and the target class ...

Federated Learning (FL) is increasingly vulnerable to model poisoning attacks, where malicious clients degrade the global model's accuracy with manipulated updates. Unfortunately, most existing defenses struggle to handle the scenarios when multiple ...

Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server. However, existing FL methods still face challenges when dealing with scarce and label-...

Community detection techniques are useful for social media platforms to discover tightly connected groups of users who share common interests. However, this functionality often comes at the expense of potentially exposing individuals to privacy breaches ...

Prior attacks on graph neural networks have focused on graph poisoning and evasion, neglecting the network's weights and biases. For convolutional neural networks, however, the risk arising from bit flip attacks is well recognized. We show that the ...

Federated Learning (FL) is susceptible to poisoning attacks, wherein compromised clients manipulate the global model by modifying local datasets or sending manipulated model updates. Experienced defenders can readily detect and mitigate the poisoning ...

Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks. The social graph, constructed from social network interactions, contains benign and bot accounts that influence each other. However, previous graph-...

Deep visual graph matching (GM) is a challenging combinatorial task that involves finding a permutation matrix that indicates the correspondence between keypoints from a pair of images. Like many learning systems, empirical studies have shown that visual ...

An irregular tensor is a collection of matrices with different numbers of rows. Real-world data from diverse domains, including medical and stock data, are effectively represented as irregular tensors due to the inherent variations in data length. For ...

Recently, Large Language Model (LLM)-empowered recommender systems (RecSys) have brought significant advances in personalized user experience and have attracted considerable attention. Despite the impressive progress, the research question regarding the ...