Businesses are naturally interested in detecting anomalies in their internal processes, because these can be indicators for fraud and inefficiencies. Within the domain of business intelligence, classic anomaly detection is not very frequently researched. In this paper, we propose a method, using autoencoders, for detecting and analyzing anomalies occurring in the execution of a business process. Our method does not rely on any prior knowledge about the process and can be trained on a noisy dataset already containing the anomalies. We demonstrate its effectiveness by evaluating it on 700 different datasets and testing its performance against three state-of-the-art anomaly detection methods. This paper is an extension of our previous work from 2016 ( in Unsupervised anomaly detection in noisy business process event logs using denoising autoencoders. In: International conference on discovery science, Springer, pp 442---456, 2016). Compared to the original publication we have further refined the approach in terms of performance and conducted an elaborate evaluation on more sophisticated datasets including real-life event logs from the Business Process Intelligence Challenges of 2012 and 2017. In our experiments our approach reached an @math F1 score of 0.87, whereas the best unaltered state-of-the-art approach reached an @math F1 score of 0.72. Furthermore, our approach can be used to analyze the detected anomalies in terms of which event within one execution of the process causes the anomaly.
Detection methods based on n-gram models have been widely studied for the identification of attacks and malicious software. These methods usually build on one of two learning schemes: anomaly detection, where a model of normality is constructed from n-grams, or classification, where a discrimination between benign and malicious n-grams is learned. Although successful in many security domains, previous work falls short of explaining why a particular scheme is used and more importantly what renders one favorable over the other for a given type of data. In this paper we provide a close look on n-gram models for intrusion detection. We specifically study anomaly detection and classification using n-grams and develop criteria for data being used in one or the other scheme. Furthermore, we apply these criteria in the scope of web intrusion detection and empirically validate their effectiveness with different learning-based detection methods for client-side and service-side attacks. A method for anomaly detection is introduced in which normal'' is defined by short-range correlations in a process' system calls. Initial experiments suggest that the definition is stable during normal behavior for standard UNIX programs. Further, it is able to detect several common intrusions involving sendmail and lpr. This work is part of a research program aimed at building computer security systems that incorporate the mechanisms and algorithms used by natural immune systems. This survey attempts to provide a comprehensive and structured overview of the existing research for the problem of detecting anomalies in discrete symbolic sequences. The objective is to provide a global understanding of the sequence anomaly detection problem and how existing techniques relate to each other. The key contribution of this survey is the classification of the existing research into three distinct categories, based on the problem formulation that they are trying to solve. These problem formulations are: 1) identifying anomalous sequences with respect to a database of normal sequences; 2) identifying an anomalous subsequence within a long sequence; and 3) identifying a pattern in a sequence whose frequency of occurrence is anomalous. We show how each of these problem formulations is characteristically distinct from each other and discuss their relevance in various application domains. We review techniques from many disparate and disconnected application domains that address each of these formulations. Within each problem formulation, we group techniques into categories based on the nature of the underlying algorithm. For each category, we provide a basic anomaly detection technique, and show how the existing techniques are variants of the basic technique. This approach shows how different techniques within a category are related or different from each other. Our categorization reveals new variants and combinations that have not been investigated before for anomaly detection. We also provide a discussion of relative strengths and weaknesses of different techniques. We show how techniques developed for one problem formulation can be adapted to solve a different formulation, thereby providing several novel adaptations to solve the different problem formulations. We also highlight the applicability of the techniques that handle discrete sequences to other related areas such as online anomaly detection and time series anomaly detection.