BINet: Multi-perspective Business Process Anomaly Classification

In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level but also on event attribute level. Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs. BINet outperforms all other methods both on the synthetic as well as on the real-life datasets

Role based behavior analysis

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Nos nossos dias, o sucesso de uma empresa depende da sua agilidade e capacidade de se adaptar a condições que se alteram rapidamente. Dois requisitos para esse sucesso são trabalhadores proactivos e uma infra-estrutura ágil de Tecnologias de Informacão/Sistemas de Informação (TI/SI) que os consiga suportar. No entanto, isto nem sempre sucede. Os requisitos dos utilizadores ao nível da rede podem nao ser completamente conhecidos, o que causa atrasos nas mudanças de local e reorganizações. Além disso, se não houver um conhecimento preciso dos requisitos, a infraestrutura de TI/SI poderá ser utilizada de forma ineficiente, com excessos em algumas áreas e deficiências noutras. Finalmente, incentivar a proactividade não implica acesso completo e sem restrições, uma vez que pode deixar os sistemas vulneráveis a ameaças externas e internas. O objectivo do trabalho descrito nesta tese é desenvolver um sistema que consiga caracterizar o comportamento dos utilizadores do ponto de vista da rede. Propomos uma arquitectura de sistema modular para extrair informação de fluxos de rede etiquetados. O processo é iniciado com a criação de perfis de utilizador a partir da sua informação de fluxos de rede. Depois, perfis com características semelhantes são agrupados automaticamente, originando perfis de grupo. Finalmente, os perfis individuais são comprados com os perfis de grupo, e os que diferem significativamente são marcados como anomalias para análise detalhada posterior. Considerando esta arquitectura, propomos um modelo para descrever o comportamento de rede dos utilizadores e dos grupos. Propomos ainda métodos de visualização que permitem inspeccionar rapidamente toda a informação contida no modelo. O sistema e modelo foram avaliados utilizando um conjunto de dados reais obtidos de um operador de telecomunicações. Os resultados confirmam que os grupos projectam com precisão comportamento semelhante. Além disso, as anomalias foram as esperadas, considerando a população subjacente. Com a informação que este sistema consegue extrair dos dados em bruto, as necessidades de rede dos utilizadores podem sem supridas mais eficazmente, os utilizadores suspeitos são assinalados para posterior análise, conferindo uma vantagem competitiva a qualquer empresa que use este sistema.In our days, the success of a corporation hinges on its agility and ability to adapt to fast changing conditions. Proactive workers and an agile IT/IS infrastructure that can support them is a requirement for this success. Unfortunately, this is not always the case. The user’s network requirements may not be fully understood, which slows down relocation and reorganization. Also, if there is no grasp on the real requirements, the IT/IS infrastructure may not be efficiently used, with waste in some areas and deficiencies in others. Finally, enabling proactivity does not mean full unrestricted access, since this may leave the systems vulnerable to outsider and insider threats. The purpose of the work described on this thesis is to develop a system that can characterize user network behavior. We propose a modular system architecture to extract information from tagged network flows. The system process begins by creating user profiles from their network flows’ information. Then, similar profiles are automatically grouped into clusters, creating role profiles. Finally, the individual profiles are compared against the roles, and the ones that differ significantly are flagged as anomalies for further inspection. Considering this architecture, we propose a model to describe user and role network behavior. We also propose visualization methods to quickly inspect all the information contained in the model. The system and model were evaluated using a real dataset from a large telecommunications operator. The results confirm that the roles accurately map similar behavior. The anomaly results were also expected, considering the underlying population. With the knowledge that the system can extract from the raw data, the users network needs can be better fulfilled, the anomalous users flagged for inspection, giving an edge in agility for any company that uses it

Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (i.e. mention-anomaly-based event detection), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users' interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events.Comment: 17 page

Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

It is estimated that between $600 and$850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to$175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors

River water-quality monitoring is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values. However, anomalies caused by technical issues confound these data, while the volume and velocity of data prevent manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data. After identifying end-user needs and defining anomalies, we ranked their importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, using other water-quality variables as covariates reduced performance due to complex relationships among variables. Classification of drift and periods of anomalously low or high variability improved when we applied replaced anomalous measurements with forecasts, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies, but were also less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, all feature-based methods produced low false positive rates, but did not and require training or optimization. Rule-based methods successfully detected impossible values and missing observations. Thus, we recommend using a combination of methods to improve anomaly detection performance, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and analysts for optimal outcomes with respect to both detection performance and end-user needs. Our framework is applicable to other types of high frequency time-series data and anomaly detection applications