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Fraunhofer AISEC's Projects

a3 icon a3

Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.

archie icon archie

ARCHIE is a QEMU-based architecture-independent fault evaluation tool, that is able to simulate transient and permanent instruction and data faults in RAM, flash, and processor registers.

argue icon argue

Anomaly Detection by Recombining Gated Unsupervised Experts

avus icon avus

Lightweight tool for re-prioritizing vulnerability findings

cmc icon cmc

The Connector Measurement Component (CMC) repository provides tools and software to enable remote attestation of computing platforms in the International Data Spaces (IDS).

codyze icon codyze

Codyze is a static analyzer for Java, C, C++ based on code property graphs

cpg icon cpg

A library to extract Code Property Graphs from C/C++, Java, Go, Python, Ruby and every other language through LLVM-IR.

cpg-go-ast icon cpg-go-ast

A cgo wrapper for go's ast package. Part of the code property graph project.

cpg-neo4j icon cpg-neo4j

Neo4J visualisation tool for the Code Property Graph

d3-package-dependency icon d3-package-dependency

Visualization of Java package dependencies in OSGi environments using D3/Hierarchical Edge Bundling

da3d icon da3d

Double-Adversarial Activation Anomaly Detection

data icon data

Differential Address Trace Analysis

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