19,612 research outputs found
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
Lightweight Multilingual Software Analysis
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
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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