Anomaly detection is disclosed, including: determining a set of anomalous events associated with an enterprise network; and determining a path of interest based at least in part on at least a subset of the set of anomalous events.

Anomaly detection system for enterprise network security

Model-based security testing relies on models to test whether a software system meets its security requirements. It is an active research field of high relevance for industrial applications, with many approaches and notable results published in recent years. This article provides a taxonomy for model-based security testing approaches. It comprises filter criteria i.e.i¾?model of system security, security model of the environment and explicit test selection criteria as well as evidence criteria i.e.i¾?maturity of evaluated system, evidence measures and evidence level. The taxonomy is based on a comprehensive analysis of existing classification schemes for model-based testing and security testing. To demonstrate its adequacy, 119 publications on model-based security testing are systematically extracted from the five most relevant digital libraries by three researchers and classified according to the defined filter and evidence criteria. On the basis of the classified publications, the article provides an overview of the state of the art in model-based security testing and discusses promising research directions with regard to security properties, coverage criteria and the feasibility and return on investment of model-based security testing. Copyright © 2015 John Wiley & Sons, Ltd.

https://qe-informatik.uibk.ac.at/wp-content/uploads/2014/03/Felderer_et_al-2015-Software_Testing_Verification_and_Reliability.pdf

Model-based security testing: a taxonomy and systematic classification

Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. To overcome this disadvantage and inspired by the recent success of deep learning, we propose the application of deep autoencoder neural networks to detect anomalous journal entries. We demonstrate that the trained network's reconstruction error obtainable for a journal entry and regularized by the entry's individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment. Experiments on two real-world datasets of journal entries show the effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A) and 16.95 (dataset B) and less false positive alerts compared to state of the art baseline methods. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.

Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks

Reducing false positives in fraud detection: Combining the red flag approach with process mining

Anomalous activity is detected using event information that is received from accounts from within an online service. Generally, anomalous activity is detected by comparing a baseline profile that includes past event information for accounts of the online service with a recent profile that includes recent event information for the accounts. Anomalous activity is detected when the recent profile shows that one or more events are occurring more frequently as compared to the occurrence of the event the associated baseline profile. The events that are recorded and used in the anomaly detection may include all or a portion of events that are monitored by the online service. One or more reports may also be automatically generated and provided to one or more users to show activity that may be considered anomalous activity.

Detecting anomalous activity from accounts of an online service

ERP systems generally implement controls to prevent certain common kinds of fraud. In addition however, there is an imperative need for detection of more sophisticated patterns of fraudulent activity as evidenced by the legal requirement for company audits and the common incidence of fraud. This paper describes the design and implementation of a framework for detecting patterns of fraudulent activity in ERP systems. We include the description of six fraud scenarios and the process of specifying and detecting the occurrence of those scenarios in ERP user log data using the prototype software which we have developed. The test results for detecting these scenarios in log data have been verified and confirm the success of our approach which can be generalized to ERP systems in general.

/pdf/fraud-detection-in-erp-systems-using-scenario-matching-4i4hnwqilc.pdf

Fraud Detection in ERP Systems Using Scenario Matching

Systems, methods and articles for determining anomalous user activity are disclosed. Data representing a transaction activity corresponding to a plurality of user transactions can be received and user transactions can be grouped according to types of user transactions. The transaction activity can be determined to be anomalous in relation to the grouped user transactions based on a predetermined parameter.

Detecting Anomalous User Activity

Owner-controlled access control to released data

Customer churn risk scores are based on a multi-variable churn risk model relating customer and customer account characteristics to a risk of customer churn. A computer-implemented method of generating and presenting churn risk scores of customers of a telecommunication provider involves analyzing, on an in-memory database platform, customer call data records and customer records to calculate a churn likelihood value, an influence factor value, and an average spend value for each customer. The method assigns a churn risk score to each customer according to the model using the calculated churn likelihood value, the calculated influence factor value, and the calculated average spend value as input to the model. The churn risk scores for one or more customers are displayed visually on an interactive computer-user interface (UI).

Churn risk scoring using call network analysis

As technology advances, fraud is becoming increasingly complicated and difficult to detect, especially when individuals collude. Surveys show that the median loss from collusive fraud is much greater than fraud perpetrated by individuals. Despite its prevalence and potentially devastating effects, internal auditors often fail to consider collusion in their fraud assessment and detection efforts. This paper describes a system designed to detect collusive fraud in enterprise resource planning (ERP) systems. The fraud detection system aggregates ERP, phone and email logs to detect collusive fraud enabled via phone and email communications. The performance of the system is evaluated by applying it to the detection of six fraudulent scenarios involving collusion.

/pdf/detecting-collusive-fraud-in-enterprise-resource-planning-1ny7fqt9jj.pdf

Detecting Collusive Fraud in Enterprise Resource Planning Systems

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