volatility | An advanced memory forensics framework
kandi X-RAY | volatility Summary
kandi X-RAY | volatility Summary
Volatility Framework - Volatile memory extraction utility framework. The Volatility Framework is a completely open collection of tools, implemented in Python under the GNU General Public License, for the extraction of digital artifacts from volatile memory (RAM) samples. The extraction techniques are performed completely independent of the system being investigated but offer visibilty into the runtime state of the system. The framework is intended to introduce people to the techniques and complexities associated with extracting digital artifacts from volatile memory samples and provide a platform for further work into this exciting area of research. The Volatility distribution is available from: Volatility should run on any platform that supports Python (
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Top functions reviewed by kandi - BETA
- Calculate the kernel functions
- Returns a list of kernel symbols
- Determine if a call reference is modified
- Find the kernel with given address
- Create a MacProfile from the given file
- Parse dsymutil output
- Return the mac_types
- Calculate the compressor
- Write to the given buffer
- Calculate the suggested profile
- Render a text file
- Calculate local calendar events
- Perform OS scan scan
- Calculate registers
- Get the available pages
- Calculate the MAC stats
- Calculate memory capture
- Decodes the input buffer
- Calculates the mount table
- Calculate the kernel modules
- Generate suggested suggestions
- Render thread information
- Renders a text file with the given data
- Calculate timestamps
- Calculate thread range
- Calculate the Windows Registry version
volatility Key Features
volatility Examples and Code Snippets
Lines of Code : 107License : Strong Copyleft (GPL-3.0)
Copy"pairlists": [
{"method": "StaticPairList"}
],
"pairlists": [
{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"min_value": 0,
"refresh_period": 1800
}
],
"pairlists": 
License : No LicenseCopyimport PyHeston
import numpy
import matplotlib.pyplot as plt
import itertools
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
help(PyHeston.HestonMixedGaussianCall)
Help on built-in function HestonMixedGaussianCall in module PyHest ---------------------------------
Module SigCheck
---------------------------------
Aims to validate Authenticode-signed processes, either with embedded signature or catalog-signed
Options:
--catalog [dir]: directory containing catalog files (.c Community Discussions
Trending Discussions on volatility
QUESTION
find the code attached below, so the ReadCBOE function reads information from investing.com and saves it to string str, Update news function then analyzes the information obtained from ReadCBOE and stores it into relevant arrays, the code is running without any errors just cant figure out why information is not being stored correctly into the arrays. ps the same code works perfectly on mt4
...ANSWER
Answered 2021-Jun-10 at 14:51Try the following code which should work for larger strings.
QUESTION
I would like to ask you please about how to create one graph with two diffrent y axis?
...ANSWER
Answered 2021-Jun-03 at 09:45I create a dataframe with 2 columns 'a','b'. Each contains 100 random numbers
QUESTION
The following data was imported by left clicking the file on the folder pane to bring up the import window and imported as a cell array. Each column is going to be one of my variables (K = 1st column etc).
...ANSWER
Answered 2021-Jun-02 at 18:06I'm not sure about that function in particular, but most functions can take vectorized input, it's a really useful feature. That is to say, where functions in other languages take single value inputs, matlab thinks of everything as arrays automatically, so you can pass vectors to functions instead, and it calls the function on each row in the input.
For instance,
QUESTION
I am desperately trying to get the selected nodes from angular tree in JSON nested format. So far I managed to get the selected array of flat nodes with this.checklistSelection.selected. But what I need, I need to get the selected nodes in JSON format, with all nested JSON objects by their level.
ANSWER
Answered 2021-May-28 at 15:50In order to build a tree, you need to pre-process your data by assigning IDs to each of your items. You can use a stack to keep track of the relationships as you assign them.
You can accomplish this in phases:
- Assign
idandparentIdkeys for each item (applyRelationships) - Convert the flat array into a tree (
listToTree) - Convert the tree into an object (
treeToObject)
In the original example, I brute-forced the nesting of each object by setting max-depth. I did not utilize the expandable property. In this modified example, I ditched the maxDepth paramater.
QUESTION
if I have a data frame of historic option data;
...ANSWER
Answered 2021-May-16 at 04:43You can use any of the apply function here -
QUESTION
Trying to run a OLS regression model in R.
...ANSWER
Answered 2021-May-13 at 22:39It should have the data because the columns volt, lfquantBS, etc. exist only within the frame of the data.frame object named 'data'. In addition, case is important. In the formula, there is lfquantBS while in the dataset, it is named as LfQuantBS
QUESTION
I'm writing an R shiny application. I'm facing much trouble, particularly the checkboxGroupInput function. I'm hoping that I will be able to create a dynamic list that will automatically list down all columns except the first column, source_file$Date of a dataset named source_file, and I'm not entirely sure on it. Would greatly appreciate any help you can provide!
Sample dataset of source_file would look something like this:
Date Index 1 Index 2 Index 3 Index 4 Index 5 2016-01-01 +5% -2% +5% +10% +12% 2016-01-08 +3% +13% -8% -3% +10% 2016-01-15 +2% +11% -3% +4% -15%The end goal is that I hope the checkboxGroupInput function will be able to automatically read all columns starting from the second column (ignore Date). In this case, the check box would load up 5 options, Index 1 to Index 5. It should be replicable such that it can load any number of indexes depending on the data specified. I tried hard-coding each individual index in but it's definitely counter-intuitive and so frustrating to do.
...ANSWER
Answered 2021-May-11 at 07:29You can try something like the following which uses colnames() to extract the new choices, and then updates the checkboxGroupInput with updateCheckboxGroupInput():
QUESTION
I am trying to transform a table of data--I want the rows to become the columns, and the columns to become the rows (like a total complete pivot). I am using the method from this answer to do so.
If it makes any difference, I am running my own SQL server on a Raspberry Pi using PHPMyAdmin.
The table (truncated) looks like this:
...ANSWER
Answered 2021-May-05 at 20:13You seem to be suggesting that Grant is a column name -- bad choice, but you are stuck with it. The standard way to escape names is to use double quotes:
QUESTION
I want to estimate adjust a volatility weighted time series of returns with a Garch 1,1 model in MATLAB. When I run the code, however, using the standard MATLAB functions I hit a wall in generating the proper return vectors I'm looking for to build off of it further.
Does anyone have a Garch 1,1 model that can run within a function and output the adjusted volatility series as a vector?
...ANSWER
Answered 2021-Apr-20 at 19:31here's the model
QUESTION
I am actually trying to use the IPOPT Optimisor available on GEKKO in order to optimise a large non-convex and non-linear problem.In order to do that I need to use the Fast Fourrier Transform with scipy.First,lets fix our sample data(for simplicity):
...ANSWER
Answered 2021-Apr-19 at 20:11Gekko requires that expressions are not black box but are able to be expressed with special types of variables (Gekko type) for automatic differentiation and sparsity detection. This may be better solved with a solver such as Scipy.optimize.minimize. Here is a comparison of the two on a simple problem.
Scipy
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Install volatility
Unpack the latest version of Volatility from volatilityfoundation.org
To see available options, run "python vol.py -h" or "python vol.py --info" Example:
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