dependency propagation analyser on graphs
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cause: causality analysis part, implementation of custom DAS, the code to generate two files with CVE related features (one-hop neighbor, two-hop neighbor)
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cent: three centrality measurement methods: degree (three directions), betweenness, and eigenvector
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data: extracted other format data sets
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exp: the exploration code on different files, code to call diverse centrality measurement, notebooks to visualize data and perform stastical analysis
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process: the code to call neo4j and export other formats of graphs, like graphml and csv
- How to install Goblin Weaver
java -Dneo4jUri="bolt://localhost:7687/" -Dneo4jUser="neo4j" -Dneo4jPassword="password" -jar goblinWeaver-2.1.0.jar
- configuration of neo4j.conf: add the following lines to conf file to enable apoc output
dbms.security.procedures.unrestricted=apoc.*
dbms.security.procedures.allowlist=apoc.*
apoc.export.file.enabled=true
- run script:
# export dump into graphml and csv formats
python3 data_export.py
(tested on macOS and Ubuntu 20.04.5 LTS for small-scale data)
# configure virtualenv environment
curl https://pyenv.run | bash
export PYENV_ROOT="$HOME/.pyenv"
[[ -d $PYENV_ROOT/bin ]] && export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"
# specify python version
pyenv install 3.10
pyenv global 3.10
# create local environment
pyenv virtualenv 3.10 DPAnalyzer
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"
pyenv activate DPAnalyzer
# download dependencies
pip3 install -r requirmentst.txt
# centrality measurement --- comment different parts to run different measurements
python3 cent_cal.py
# causality analysis
python3 cau_dis.py