19,612 research outputs found

    BINet: Multi-perspective Business Process Anomaly Classification

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    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

    Lightweight Multilingual Software Analysis

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    Developer preferences, language capabilities and the persistence of older languages contribute to the trend that large software codebases are often multilingual, that is, written in more than one computer language. While developers can leverage monolingual software development tools to build software components, companies are faced with the problem of managing the resultant large, multilingual codebases to address issues with security, efficiency, and quality metrics. The key challenge is to address the opaque nature of the language interoperability interface: one language calling procedures in a second (which may call a third, or even back to the first), resulting in a potentially tangled, inefficient and insecure codebase. An architecture is proposed for lightweight static analysis of large multilingual codebases: the MLSA architecture. Its modular and table-oriented structure addresses the open-ended nature of multiple languages and language interoperability APIs. We focus here as an application on the construction of call-graphs that capture both inter-language and intra-language calls. The algorithms for extracting multilingual call-graphs from codebases are presented, and several examples of multilingual software engineering analysis are discussed. The state of the implementation and testing of MLSA is presented, and the implications for future work are discussed.Comment: 15 page

    Combining Graph-Based and Deduction-Based Information-Flow Analysis

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    Information flow control (IFC) is a category of techniques for ensuring system security by enforcing information flow properties such as non-interference. Established IFC techniques range from fully automatic approaches with much over-approximation to approaches with high pre- cision but potentially laborious user interaction. A noteworthy approach mitigating the weaknesses of both automatic and interactive IFC tech- niques is the hybrid approach, developed by Küsters et al., which – how- ever – is based on program modifications and still requires a significant amount of user interaction. In this paper, we present a combined approach that works without any program modifications. It minimizes potential user interactions by apply- ing a dependency-graph-based information-flow analysis first. Based on over-approximations, this step potentially generates false positives. Pre- cise non-interference proofs are achieved by applying a deductive theorem prover with a specialized information-flow calculus for checking that no path from a secret input to a public output exists. Both tools are fully integrated into a combined approach, which is evaluated on a case study, demonstrating the feasibility of automatic and precise non-interference proofs for complex programs
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