Abstract
The high asymmetry of international termination rates is fertile ground for the appearance of fraud in telecom companies. International calls have higher values when compared with national ones, which raises the attention of fraudsters. In this paper, we present a solution for a real problem called interconnect bypass fraud, more specifically, a newly identified distributed pattern that crosses different countries and keeps fraudsters from being tracked by almost all fraud detection techniques. This problem is one of the most expressive in the telecommunication domain, and it has some abnormal behaviours like the occurrence of a burst of calls from specific numbers. Based on this assumption, we propose the adoption of a new fast forgetting technique that works together with the Lossy Counting algorithm. We apply frequent set mining to capture distributed patterns from different countries. Our goal is to detect as soon as possible items with abnormal behaviours, e.g., bursts of calls, repetitions, mirrors, distributed behaviours and a small number of calls spread by a vast set of destination numbers. The results show that the application of different techniques improves the detection ratio and not only complements the techniques used by the telecom company but also improves the performance of the Lossy Counting algorithm in terms of run-time, memory used and sensibility to detect the abnormal behaviours. Additionally, the application of frequent set mining allows us to capture distributed fraud patterns.

Abstract
Fraud in telephony incurs huge revenue losses and causes a menace to both the service providers and legitimate users. This problem is growing alongside augmenting technologies. Yet, the works in this area are hindered by the availability of data and confidentiality of approaches. In this work, we deal with the problem of detecting different types of unsolicited users from spammers to fraudsters in a massive phone call network. Most of the malicious users in telecommunications have some of the characteristics in common. These characteristics can be defined by a set of features whose values are uncommon for normal users. We made use of graph-based metrics to detect profiles that are significantly far from the common user profiles in a real data log with millions of users. To achieve this, we looked for the high leverage points in the 99.99th percentile, which identified a substantial number of users as extreme anomalous points. Furthermore, clustering these points helped distinguish malicious users efficiently and minimized the problem space significantly. Convincingly, the learned profiles of these detected users coincided with fraudulent behaviors.

Abstract
Detecting anomalies in Waste transportation networks is vital for uncovering illegal or unsafe activities, that can have serious environmental and regulatory consequences. Identifying anomalies in such networks presents a significant challenge due to the limited availability of labeled data and the subtle nature of illicit activities. Moreover, traditional anomaly detection methods relying solely on individual transaction data may overlook deeper, network-level irregularities that arise from complex interactions between entities, especially in the absence of labeled data. This study explores anomaly detection in a waste transport network using unsupervised learning, enhanced by limited supervision and enriched with network structure information. Initially, unsupervised models like Isolation Forest, K-Means, LOF, and Autoencoders were applied using statistical and graph-based features. These models detected outliers without prior labels. Later, information on a few confirmed anomalous users enabled weak supervision, guiding feature selection through statistical tests like Kolmogorov-Smirnov and Anderson-Darling. Results show that models trained on a reduced, graph-focused feature set improved anomaly detection, particularly under extreme class imbalance. Isolation Forest notably ranked known anomalies highly. Ego network visualizations supported these findings, demonstrating the value of integrating structural features and limited labels for identifying subtle, relational anomalies.

Abstract

Abstract

Abstract
Waste transport management is a critical sector where maintaining accurate records and preventing fraudulent or illegal activities is essential for regulatory compliance, environmental protection, and public safety. However, monitoring and analyzing large-scale waste transport records to identify suspicious patterns or anomalies is a complex task. These records often involve multiple entities and exhibit variability in waste flows between them. Traditional anomaly detection methods relying solely on individual transaction data, may struggle to capture the deeper, network-level anomalies that emerge from the interactions between entities. To address this complexity, we propose a hybrid approach that integrates network-based measures with machine learning techniques for anomaly detection in waste transport data. Our method leverages advanced graph analysis techniques, such as sub-graph detection, community structure analysis, and centrality measures, to extract meaningful features that describe the network's topology. We also introduce novel metrics for edge weight disparities. Further, advanced machine learning techniques, including clustering, neural network, density-based, and ensemble methods are applied to these structural features to enhance and refine the identification of anomalous behaviors.