ML-IDS | An IDS implementation using machine learning | Machine Learning library

by Belval Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | ML-IDS Summary

ML-IDS is a Python library typically used in Artificial Intelligence, Machine Learning, Keras applications. ML-IDS has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However ML-IDS build file is not available. You can download it from GitHub.

An IDS implementation using machine learning

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Quality

Security

License

Reuse

Support

ML-IDS has a low active ecosystem.

It has 23 star(s) with 11 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

ML-IDS has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ML-IDS is current.

Quality

ML-IDS has 0 bugs and 0 code smells.

Security

ML-IDS has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ML-IDS code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ML-IDS is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

ML-IDS releases are not available. You will need to build from source code and install.

ML-IDS has no build file. You will be need to create the build yourself to build the component from source.

ML-IDS saves you 113 person hours of effort in developing the same functionality from scratch.

It has 285 lines of code, 16 functions and 6 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed ML-IDS and discovered the below as its top functions. This is intended to give you an instant insight into ML-IDS implemented functionality, and help decide if they suit your requirements.

Train the model
Returns the next train batch
Returns the ground truth length
Returns the batch size
Returns the length of the feature vector
Get train data
Returns the number of train data
Resets the current batch point
Get test data
Returns the number of examples in the train
Parse command line arguments
Trains the model

Get all kandi verified functions for this library.

ML-IDS Key Features

No Key Features are available at this moment for ML-IDS.

ML-IDS Examples and Code Snippets

No Code Snippets are available at this moment for ML-IDS.

Community Discussions

Trending Discussions on ML-IDS

Does RandomStringUtils create a deterministic or reproducible sequence?

QUESTION

Does RandomStringUtils create a deterministic or reproducible sequence?

Asked 2020-Dec-12 at 13:20

On a Java application server we use RandomStringUtils.randomAlphabetic() to sequentially create HTML-IDs for recurring similar paragraphs in an iterating manner.

Via these generated IDs, we allow for an inner-page anchor navigation (without a page load triggered upon click).

Now the question is, can I use these generated IDs also for URLs linking from external pages to this target? According to my observation, subsequent requests to the same page, create the same sequence of IDs. If the generated sequence of randomAlphabetic is indeed predictable (also over multiple page loads), we could not only use these links for inner-page navigation, but also for links referring from outside, since the first, second, third, ... generated ID would always be the same.

...

ANSWER

Answered 2020-Dec-12 at 13:20

If the "random" ID is always the same for a given text string of a paragraph (that is, the ID is solely a function of a paragraph's text), then you should consider hash functions, rather than pseudorandom number generators such as randomAlphabetic. (I assume that for your use case of identifying parts of a page, the risk of generating duplicate IDs for different text strings is negligible. If you can't tolerate this risk, though, hash functions should not be used.) There are many kinds of hash functions for this purpose; even java.lang.String.hashCode() will work here since the Java documentation for this method specifies the exact algorithm it uses.

Moreover, randomAlphabetic is not "deterministic" in the sense you want for two reasons:

You can't set the seed of the underlying generator the method uses (RandomStringUtils stores a static PRNG variable initialized with an undefined seed, namely new Random()).
The documentation for RandomStringUtils.randomAlphabetic (among other RandomStringUtils methods) doesn't specify the exact algorithm it uses to generate random strings.

Source https://stackoverflow.com/questions/64908600

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ML-IDS

You can download it from GitHub.
You can use ML-IDS 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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