Related papers: DELATOR: Money Laundering Detection via Multi-Task…

The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the…

Computing is still under a significant threat from ransomware, which necessitates prompt action to prevent it. Ransomware attacks can have a negative impact on how smart grids, particularly digital substations. In addition to examining a…

State-of-the-art machine-learning methods for event cameras treat events as dense representations and process them with conventional deep neural networks. Thus, they fail to maintain the sparsity and asynchronous nature of event data,…

Network analysis and machine learning techniques have been widely applied for building malware detection systems. Though these systems attain impressive results, they often are $(i)$ not extensible, being monolithic, well tuned for the…

Anomaly detection is a crucial task in complex distributed systems. A thorough understanding of the requirements and challenges of anomaly detection is pivotal to the security of such systems, especially for real-world deployment. While…

Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a node's local structure on predictions. This paper…

Malicious software (malware) poses an increasing threat to the security of communication systems as the number of interconnected mobile devices increases exponentially. While some existing malware detection and classification approaches…

Graph-based anomaly detection is pivotal in diverse security applications, such as fraud detection in transaction networks and intrusion detection for network traffic. Standard approaches, including Graph Neural Networks (GNNs), often…

The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing…

The digital revolution has significantly impacted financial transactions, leading to a notable increase in credit card usage. However, this convenience comes with a trade-off: a substantial rise in fraudulent activities. Traditional machine…

Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph Convolutional Networks (GCNs) are widely used for financial…

Graph Anomaly Detection (GAD) has recently become a hot research spot due to its practicability and theoretical value. Since GAD emphasizes the application and the rarity of anomalous samples, enriching the varieties of its datasets is…

We present a data mining approach for profiling bank clients in order to support the process of detection of anti-money laundering operations. We first present the overall system architecture, and then focus on the relevant component for…

Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this…

With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or…

Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more.…

Ransomware can produce direct and controllable economic loss, which makes it one of the most prominent threats in cyber security. As per the latest statistics, more than half of malwares reported in Q1 of 2017 are ransomwares and there is a…

Adversarial attacks can affect the performance of existing deep learning models. With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to…

The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive…

The Inland Revenue Services is overwhelmed with gigabyte of disk capacity containing data about tax payers in the state. The data stored on the database increases in size at an alarming rate. This has resulted in a data rich but information…