pytorch-adversarial_box | PyTorch library for adversarial attack | Cybersecurity library

 by   wanglouis49 Python Version: Current License: No License

kandi X-RAY | pytorch-adversarial_box Summary

kandi X-RAY | pytorch-adversarial_box Summary

pytorch-adversarial_box is a Python library typically used in Security, Cybersecurity, Pytorch, Generative adversarial networks applications. pytorch-adversarial_box has no bugs, it has no vulnerabilities and it has low support. However pytorch-adversarial_box build file is not available. You can download it from GitHub.

PyTorch library for adversarial attack and training
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            kandi-support Support

              pytorch-adversarial_box has a low active ecosystem.
              It has 108 star(s) with 42 fork(s). There are 4 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 23 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of pytorch-adversarial_box is current.

            kandi-Quality Quality

              pytorch-adversarial_box has 0 bugs and 18 code smells.

            kandi-Security Security

              pytorch-adversarial_box has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pytorch-adversarial_box code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              pytorch-adversarial_box does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              pytorch-adversarial_box releases are not available. You will need to build from source code and install.
              pytorch-adversarial_box has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              pytorch-adversarial_box saves you 156 person hours of effort in developing the same functionality from scratch.
              It has 389 lines of code, 19 functions and 8 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pytorch-adversarial_box and discovered the below as its top functions. This is intended to give you an instant insight into pytorch-adversarial_box implemented functionality, and help decide if they suit your requirements.
            • Performs an MNIST Bbox substitution
            • Evaluate the model on the given loader
            • Perform the jacobian augmented training
            • Return start and end indices for a batch
            • Calculates the Jacobian for each class
            • Convert input to a Variable object
            • Perform the attack on the given test dataset
            • Calculate the prediction of a batch
            • Train a TFSM model
            • Generate a truncated normal distribution
            • Perform perturbation on X
            • P perturb the model
            • Compute the accuracy of the given model
            • Predict using adversarial training
            • Convert tensor to a Variable object
            • Calculate the prediction for the given model
            Get all kandi verified functions for this library.

            pytorch-adversarial_box Key Features

            No Key Features are available at this moment for pytorch-adversarial_box.

            pytorch-adversarial_box Examples and Code Snippets

            No Code Snippets are available at this moment for pytorch-adversarial_box.

            Community Discussions

            QUESTION

            hardware based password manager integration with device
            Asked 2021-Apr-28 at 12:48

            I am aiming to build a hardware based password manager that will store credentials like -username and passwords- externally, right now I am searching about it but I am having trouble in identifying that how will that external device integrate with browsers and websites when connected to provide the credentials stored in it. I mean what technique is used to integrate the hardware password managers to the device or browser.

            I would appreciate any sort of help and guidance from your side, Thanks!

            ...

            ANSWER

            Answered 2021-Apr-28 at 12:48

            Usually they inject passwords using a HID device acting as a keyboard. Check out the OnlyKey as an example.

            The way these work is by injecting/typing username and password based on pressing a hardware button against which you have stored the relevant credentials. There is also the option to complete MFA by storing an OTP token. Some will act like any other password manager by parsing the website URL against what is stored, but I guess this opens an attack surface when feeding data back to the device.

            -- BVS

            Source https://stackoverflow.com/questions/67290550

            QUESTION

            What does "assumptions" refer to when writing a pentest report?
            Asked 2021-Apr-16 at 15:25

            I have to write the "assumptions" part of a pentest report and I am having trouble understanding what I should write. I checked multiple pentest reports (from https://github.com/juliocesarfort/public-pentesting-reports) but none of them had this paragraph.
            Also I found this explanation "In case there are some assumptions that the pen-tester considers before or during the test, the assumptions need to be clearly shown in the report. Providing the assumption will help the report audiences to understand why penetration testing followed a specific direction.", but still what I do have in mind it is more suited for "attack narative".
            Can you provide me a small example (for one action, situation) so I can see exactly how it should be written?

            ...

            ANSWER

            Answered 2021-Apr-16 at 15:25

            I would think the "assumptions" paragraph and the "Attack narrative" paragraph are somehow overlapping. I would use the "Assumptions" paragraph to state a couple of high level decisions made before starting the attack, with whatever little information the pentester would have on the attack. I would expand on the tools and techniques used in the "Attack narrative" paragraph

            For example an assumption could be: "The pentester is carrying on the exercise against the infrastructure of a soho company with less than 5 people It is common for soho companies to use consumer networking equipment that is usually unsecure, and left configured as defualt. For this reason the attacker focused on scanning for http and ssh using a database of vendors default username and passwords"

            Source https://stackoverflow.com/questions/67126985

            QUESTION

            Is there a way to use a particular C function/symbol as output by nm
            Asked 2021-Mar-10 at 23:13

            I'm trying to analyse a compiled file for cybersec learning purposes and want to use a particular function.

            Here is the output of nm --defined-only ./compiled_file:

            ...

            ANSWER

            Answered 2021-Mar-09 at 12:54

            Yes, it is possible. The point of having exported symbols in shared libraries is to be able to use them - after all. In C, you can do this either by linking the library to the application (not really an option for python), or runtime loading the library and finding the required symbol (on linux: dlopen, dlsym). The manpage example shows how to do this in C.

            Source https://stackoverflow.com/questions/66547182

            QUESTION

            How to allow XML, JSON and CSV files to be uploaded when CSP is set in the webpage
            Asked 2020-Nov-04 at 19:09

            Currently, I have set the following CSP header in the HTML file of my webpage -

            ...

            ANSWER

            Answered 2020-Nov-04 at 19:09

            The issue was caused and fixed as follows -

            The button that takes XML file as input in the HTML form has an inline event handler, which the CSP Policy was blocking, thereby blocking the upload. I moved this inline event handler to an external function and called the function. This fixed the issue and CSP is no longer blocking the function.

            Source https://stackoverflow.com/questions/64421818

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

            Vulnerabilities

            No vulnerabilities reported

            Install pytorch-adversarial_box

            You can download it from GitHub.
            You can use pytorch-adversarial_box 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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