Kakute: Information Flow Anlysis System For Big-data
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
.github
R
assembly
bin
build
calllocation
common
conf
core
data
dev
docs
examples
external
graphx
launcher
licenses
mllib-local
mllib
project
python
repl
resource-managers
sbin
sql
streaming
tools
.gitattributes
.gitignore
.travis.yml
CONTRIBUTING.md
LICENSE
NOTICE
README.md
analyzer.obj
appveyor.yml
dft.conf
get_db
pom.xml
scalastyle-config.xml

README.md

Kakute

Kakute is the first Information Flow Tracking (IFT) system for big-data. It is built on Spark, a popular big-data processing engine in both industry and academia.

Kakute provides a unified API for adding / removing tags for data and controlling IFT across hosts. We have built several applications based on Kakute for debugging, data provenance, preventing information leakage and performance optimization.

Kakute is based on a previous Information Flow Tracking framework Phosphor (Code, Paper). We have fixed some bugs, improved the performance and usability. Our optimized Phosphor can be here.

This work has been accepted by 33th Annual Computer Security Applications Conference (ACSAC'17), and you can see our design details in this paper.

For those who would like to reproduce result in the paper, you can find the dataset in data/kakute.

Building Kakute

Building Phosphor

Install dependencies

apt-get install openjdk-sdk-8 openjdk-sdk-8-source maven

Download phosphor, built it and instrument Java JDK

git clone https://github.com/hku-systems/phosphor.git
cd phosphor
mvn clean verify

Download Kakute

git clone https://github.com/hku-systems/kakute.git

Setup the correct phosphor directory in core/pom.xml

<dependency>
  <groupId>edu.columbia.cs</groupId>
  <artifactId>phosphor</artifactId>
  <version>0.0.3</version>
  <scope>system</scope>
  <systemPath>$DIRECTORY_TO_PHOSPHOR/Phosphor/target/Phosphor-0.0.3-SNAPSHOT.jar</systemPath>
</dependency>

Build Kakute, it is the same as building Spark

cd kakute
build/mvn -DskipTests clean package

Modify dft.conf according your configuration of phosphor.

dft-host = 127.0.0.1 // driver host ip
dft-port = 8787 // driver host port
dft-phosphor-java = $DIRECTORY_TO_PHOSPHOR/Phosphor/target/
dft-phosphor-jar = $DIRECTORY_TO_PHOSPHOR/Phosphor/target/Phosphor-0.0.3-SNAPSHOT.jar
dft-phosphor-cache = $DIRECTORY_FOR_CACHE
graph_dump_path = graph.dump
dft-tracking = rule
dft-input-taint = false
dft-scheme = true

Congrats. You have finished building Kakute, you can try with a simple example below.

Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".

For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1000:

scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1000:

>>> sc.parallelize(range(1000)).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run tests for a module, or individual tests.

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

## Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.