tram | source platform designed to advance research | Cybersecurity library
kandi X-RAY | tram Summary
kandi X-RAY | tram Summary
Threat Report ATT&CK Mapping (TRAM) is an open-source platform designed to advance research into automating the mapping of cyber threat intelligence reports to MITRE ATT&CK. TRAM enables researchers to test and refine Machine Learning (ML) models for identifying ATT&CK techniques in prose-based cyber threat intel reports and allows threat intel analysts to train ML models and validate ML results. Through research into automating the mapping of cyber threat intel reports to ATT&CK, TRAM aims to reduce the cost and increase the effectiveness of integrating ATT&CK into cyber threat intelligence across the community. Threat intel providers, threat intel platforms, and analysts should be able to use TRAM to integrate ATT&CK more easily and consistently into their products.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of tram
tram Key Features
tram Examples and Code Snippets
Lines of Code : 24
License : No License
Copydef to_roman_numeral(num):
lookup = [
(1000, 'M'),
(900, 'CM'),
(500, 'D'),
(400, 'CD'),
(100, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
r const chainAsync = fns => {
let curr = 0;
const last = fns[fns.length - 1];
const next = () => {
const fn = fns[curr++];
fn === last ? fn() : fn(next);
};
next();
};
chainAsync([
next => {
console.log('0 seconds');
from functools import reduce
def compose(*fns):
return reduce(lambda f, g: lambda *args: f(g(*args)), fns)
add5 = lambda x: x + 5
multiply = lambda x, y: x * y
multiply_and_add_5 = compose(add5, multiply)
multiply_and_add_5(5, 2) # 15
Community Discussions
Trending Discussions on Cybersecurity
QUESTION
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 12:48Usually 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
QUESTION
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 15:25I 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"
QUESTION
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 12:54Yes, 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.
QUESTION
Currently, I have set the following CSP header in the HTML file of my webpage -
...ANSWER
Answered 2020-Nov-04 at 19:09The 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.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install tram
Get Docker: https://docs.docker.com/get-docker/
Get Docker Compose: https://docs.docker.com/compose/install/
Ensure Docker is running. On some operating systems (e.g., MacOS), you will need to provide Docker with permissions before proceeding.
Download docker-compose.yml (view raw, save as) https://github.com/center-for-threat-informed-defense/tram/blob/master/docker/docker-compose.yml
If desired, edit the settings in docker-compose.yml
Navigate to the directory where you saved docker-compose.yml
Run TRAM using docker docker-compose -f docker-compose.yml up
Navigate to http://localhost:8000/ and login using the username and password specified in docker-compose.yml
The following steps are only required for local development and testing. The containerized version is recommended for non-developers.
Install the following packages using your OS package manager (apt, yum, homebrew, etc.): make shellcheck shfmt
Start by cloning this repository. git clone git@github.com:center-for-threat-informed-defense/tram.git
Change to the TRAM directory. cd tram/
Create a virtual environment and activate the new virtual environment. Mac and Linux python3 -m venv venv source venv/bin/activate Windows venv\Scripts\activate.bat
Install Python application requirements. pip install -r requirements/requirements.txt pip install -r requirements/test-requirements.txt pip install pre-commit
Install pre-commit hooks pre-commit install
Set up the application database. tram makemigrations tram tram migrate
Run the Machine learning training. tram attackdata load tram pipeline load-training-data tram pipeline train --model nb tram pipeline train --model logreg tram pipeline train --model nn_cls
Create a superuser (web login) tram createsuperuser
Run the application server tram runserver
Open the application in your web browser. Navigate to http://localhost:8000 and use the superuser to log in
In a separate terminal window, run the ML pipeline cd tram/ source venv/bin/activate tram pipeline run
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