clusterd | application server attack toolkit
kandi X-RAY | clusterd Summary
kandi X-RAY | clusterd Summary
clusterd is an open source application server attack toolkit. Born out of frustration with current fingerprinting and exploitation methods, clusterd automates the fingerprinting, reconnaissance, and exploitation phases of an application server attack. See the wiki for more information. The recommended installation of clusterd is to clone the Github repository. jboss fingerprint and host info. jboss DFS deployment against JBoss 5.0. jboss UNC hash retrieval. tomcat deployment and reverse shell invocation.
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Top functions reviewed by kandi - BETA
- Parse CLI arguments
- Prepare a war
- Retrieve the id of the request
- Check for authentication
- Fetches the path to the image
- Load the deployers
- Parse the nmap file
- Detects the nmap file type
- Runs a fingerprint scan on targets
- Runs the JBoss application
- Run the REST interface
- Run latter
- Check the URL for a given keyword
- Removes a file from the repository
- Undeploy a jmx console
- Removes a deployment
- Run the pre - run command
- Checks if the given IP address is supported
- Check if an error occurred
- Checks if the given IP is available on the server
- Check if an error is found
- Check if the given IP is valid
- Clean up serving directory
clusterd Key Features
clusterd Examples and Code Snippets
Community Discussions
Trending Discussions on clusterd
QUESTION
In MySQL InnoDB or lots of other database engines, the primary key is implemented with clustered index. However after searching with secondary index, the engine must look up into clustered index with primary keys provided in secondary index(if there is no covering index).
InnoDB uses B+ tree for its clustered index, it is a structure with O(log n)
complexity in searching, so we can summerize the procedure like the following:
- Using clusterd index:
One pass, Cost
O(n)
. - Using secondary index:
Two passes. The first pass cost
O(log n)
an result inm
records. Then the second pass costO(log n)
for each of them
records, so the time complexity will bem*O(log n)
.
I know when using hasing, the time complexity in seaching can be reduced to O(1)
, so I am wondering why these database engines prefer using B+ tree rather than hasing techniques(e.g. build a KV store)? Is it because of records are stored on disk rather than in memory?
Meanwhile, I have another question, some other databases, like RocksDB, use KV storage rather than B+ tree. Why they use that?
EDIT
I want to make the question more clearly. I find many tables are designed with auto increment
PK, rather than using something with actual meaning, like phone number or IP. So B+ tree's advantage is not fully exploited. For example, B+ tree is good at searching data in range, but I searching a auto increment
PK in range is rare in practice.
ANSWER
Answered 2021-Mar-09 at 12:53An efficient hash requires some fore knowledge of the type, number, distribution of the keys. Plus the complexity of handling collisions (two keys ending up with the same hash value). Space must be preallocated, and, can be too small and quickly run out, or be too big resulting in a massive waste of resources.
b-trees are efficient when small and can grow to any size provided there is the disk space available.
You cite the number of operations but b-trees use simple comparisons which are cheap, hashes use complex algorithms which are expensive. So seven or eight comparisons to find the location of a record in a 64,000 record database, probably uses less cpu than calculating a hash value.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install clusterd
You can use clusterd like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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