Sandbox jar file

You can find a list of releases, FAQs, and bugs here. Current Version: Sonification Sandbox Installer. There are a few considerations when using the GUI. First, an auditory graph must always have an x-axis defined before the player and visual grapher can display anything meaningful. To change this setting, select "type" from a channel in the "mappings" tab. Second, the GUI is designed for maximum flexibility.

It does not have many built-in tools for making the graph "sound good" at first. I can't think of any use case that would require such a jar, hence we don't release one. However, as you have noticed, we do use the examples in our QA environment. Most of the examples are automatically used as a test.

In the test environment, each example is executed, producing a PDF that is then compared with the cmp file, using iText structural comparison as well as third party tools GhostScript, ImageMagick for a pixel by pixel comparison.

We use these tests and tools internally. You have access to the code through GitHub, but we don't create a release, nor do we distribute the full test suite among others, because there are third party tools involved. If you really want to get a jar, it's really simple: get the code from GitHub, and run Maven install. A jar file will be generated in the target directory. If you want to run the test, you will have to download the required third party tools from their original location.

Update: upon reading your question a second time, I realized that you might be asking for WrapToTest , not because you want to run tests, but because you want to execute an example.

In that case, it's sufficient to remove all references to WrapToTest. Removing those references will it cause the example to produce a different result. It will only prevent that the example will be used as a test in a test environment.

I also notice that you mention XML Worker. XML Worker is a separate jar that is completely unrelated to the sandbox. OK, so my question was wrong. I had to ask for XML Worker instead of asking for the sandbox. Can you tell me where to find XML Worker? Apache Sentry will only validate whether a particular user is allowed to perform certain operation on the data. For example if a user issues a Hive query to process Student table, Apache Sentry will check whether the user has enough privileges to access Student table or not.

Apache Ranger is a centralized web based application consisting of authorization, policy administration, audit and reporting facilities.

Authorized users can access the Apache Ranger web console to manage the security policies. These security policies are deployed as lightweight processes on Namenode [ 7 ]. Kerberos is implemented in Hadoop 1. Kerberos is a conventional network authentication protocol implemented to authenticate every map reduce job that is issued by users. For every interaction with Namenode, the user and Namenode undergo a mutual authentication based on Kerberos ticketing mechanism.

Only after proper authentication, the Namenode will check whether the given user is authorized to perform the operation which has been requested. Performs only authentication. Kerberos is a conventional network authentication protocol integrate with Hadoop for authentication. In Hadoop there is no security mechanism in place to examine jar files for presence of any harmful code which is a very serious security.

Vulnerability: A legitimate user knowingly or unknowingly may execute a jar which contain harmful code that can tamper the data in the HDFS. Apart from malicious jars, there are many other vulnerabilities in Hadoop Framework including distributed cache etc.

More services can be added by the admin using add service option in the Hadoop Admin console. However the services which are added to Hadoop can be vulnerable to security attacks and in turn make entire cluster vulnerable. One such example is a ransom ware attack made on mongoDB service which was running on a Hadoop cluster. Beyond this Hadoop is vulnerable to all other security attack. Any authenticated user can run any jar file using Hadoop jar command. If the jar file contains any malicious code then it can destroy the whole cluster.

Most of the research carried out on Hadoop focused on authentication and cryptographic solutions. A legitimate user can also harm the file system and in turn corrupt the Hadoop cluster. There exists many security vulnerabilities in Hadoop Framework [ 8 ].

However the scope of this paper is confined to handling of map reduce jar files that execute on HDFS.

In existing Hadoop framework, there is no mechanism in place to validate and ensure whether a given jar file contains any harmful code or not. In this section we demonstrate a security sandbox for map reduce jobs where malicious jar files are prevented from execution and accessing HDFS [ 9 ]. This is an improvement in security of HDFS where map reduce jars are validated first and if the jar file is found to be suspicious, it is prevented from execution, thus creating a sandbox for map reduce jobs.

Implementation of sandbox security is described in Fig. Step 1. Step 2. In step 2 we need to define the characteristics based on which a jar is considered to malicious. If the jar is executed from any user account apart from root must have some limited access to the file system.

Generally a non root user will execute map reduce jobs to process files present on the HDFS. In general a non root user may not be interested in metadata about the file which include location of file blocks, recent update time stamp etc.

A non root user must not attempt to run administrator commands using File system API. Thus any such attempt is found in the mapper or reducer class then the jar file is considered to be suspicious and prevented from execution.

Step 3. In order to check whether the extracted java files are suspicious or not, the extracted java files are read by a shell script which will check whether the map reduce job was issued by user with root privileges or not and searches for the HDFS commands which are already enumerated and considered not to be executed by a non root user. Step 4. If any jar file is found to be suspicious then it is not allowed to execute on the file system.

The pseudo code of the shell script is described in Fig. Step 5. The sandbox security model can be applied on Spark program also to ensure prevention of inappropriate code from execution on HDFS. Step 6. In Hadoop framework, only map reduce code will be executed on the file system. The key finding is that the map reduce jar file may contain malicious code and Hadoop jar command will not able to detect it and allows the user to execute the jar file on the input file.

There is no mechanism to check a jar file for presence of malicious code. In our work we defined the characteristics by which a jar file can be considered to be malicious.

Any attempt to read metadata, change file permissions, attempt to run dfsadmin commands etc. We have developed our own utility to extract the map reduce jar file using apache commons library.

After extracting the java file from jar, we used a shell script to read the java files and search for the predefined, enumerated keywords and presence of any one word will make the java code suspicious and the jar is treated as malicious. The enumerated keywords include HDFS dfsadmin commands, fsck to know information about file blocks ,expunge, balancer etc. In existing Hadoop framework, a legitimate user can execute any map reduce job using Hadoop jar command and the Job tracker daemon present inside Namenode will forward the same to the respective data nodes where Task trackers will invoke a new instance of JVM for every file block to execute the map reduce job in a distributed parallel processing manner.

Our work provides a sandboxing facility where unwanted or harmful jar files can be prevented from executing on the file system. The shell script can be customized to filter jars based on requirement. This feature adds improvement to the execution environment of Hadoop framework and can be considered for inclusion in the future versions of Hadoop framework with suitable amendments.

In the Fig. Hence the finding is sandboxing technique is used to secure Hadoop framework. Sandboxing of map reduce jobs and other jar files will enhance the security of the HDFS by not allowing harmful jars to execute. Our work advances the field of study pertaining to Hadoop Security by addressing a problem which was not addressed in the previous literature.

Our work will be made available in github for reference. Our sandbox security is customizable and can be enhanced to address more security vulnerabilities in Hadoop. Sandbox security can be extended so that it can detect presence of vulnerabilities in any new service that is added to Hadoop framework.