sigma | Main Sigma Rule Repository | Security library
kandi X-RAY | sigma Summary
kandi X-RAY | sigma Summary
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Top functions reviewed by kandi - BETA
- Main entry point .
- Determine whether or not the specified yaml document matches .
- Create rule .
- Generate a query
- Convert the value pattern to its equivalent equivalent equivalent .
- Transforms a subtree tree into an OR tree .
- Parses the search and returns the index of the first token in the given list of tokens
- Generate count aggregation .
- Define a field name mapping
- Generate a map item node .
sigma Key Features
sigma Examples and Code Snippets
Lines of Code : 67License : Strong Copyleft (EUPL-1.2)
Copysigmai -t stdout -s misp --misp-url https://localhost --misp-key CAFEBABE== --misp-levels 1,2
action: global
title: 'OSINT: Emissary Panda – A potential new malicious tool'
id: 5b0562d3-8460-4482-93c4-05a3ac12042b
status: experimental
description: S apt-get install python3 python3-yaml
pip3 install -r sigma/tools/requirements.txt
pip3 install pymisp
pip3 install -r sigma/tools/requirements-misp.txt
pipenv shell
go get github.com/0xThiebaut/sigmai
def resize_images_v2(images,
size,
method=ResizeMethod.BILINEAR,
preserve_aspect_ratio=False,
antialias=False,
name=None):
"""Resize `images` t def pinv(a, rcond=None, validate_args=False, name=None):
"""Compute the Moore-Penrose pseudo-inverse of one or more matrices.
Calculate the [generalized inverse of a matrix](
https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse) using i def non_max_suppression_with_scores(boxes,
scores,
max_output_size,
iou_threshold=0.5,
score_threshold=flo Community Discussions
Trending Discussions on sigma
QUESTION
I am using the functions defined here: Extreme value analysis and user defined probability functions in Stan for modeling the data with a generalized pareto distribution, but my problem is that my model is in a for-loop and expects three real valued arguments, whereas, the gpd functions assume a vector, real, real argument.
I’m not so sure that my model chunk is so amenable to being vectorized, and so I was thinking I would need to have the gpd functions take in real valued arguments (but maybe I’m wrong).
I’d appreciate any help with switching the code around to achieve this. Here is my stan code
...ANSWER
Answered 2022-Feb-22 at 22:25Here is how the log PDF could be adapted. This way, index arrays for subsetting y into censored and non-censored observations can be passed.
QUESTION
I just learned how to insert (a limited number of) LaTeX expressions into my plot titles with
expression(). How can I generate plots containing LaTeX in their titles using a loop? For example, say I have:
ANSWER
Answered 2022-Feb-19 at 17:04We can use bquote instead of expression. This allows partial unquoting, meaning you can substitute the value of i inside the expression by wrapping it like this: .(i)
QUESTION
I am currently having issue with the implementation of the Metropolis-Hastings algorithm.
I am trying to use the algorithm to calculate integrals of the form
In using this algorithm, we can obtain a long chain of configurations ( in this case, each configuration is just a single numbers) such that in the tail-end of the chain the probability of having a particular configuration follows (or rather tends to) a gaussian distribution.
My code seems to be messing up with obtaining the said gaussian distributions. There is a strange dependence on the transition probablity (the probablity of picking a new candidate configuration depending on the previous configuration in the chain). However, if this transition probability is symmetric, there should be no dependence on this function at all (it only affects speed at which phase space [space of potential configurations] is explored and how quickly the chain converges to the desired distribution)!
In my case I am using a normal distribution transition function (which satisfies the need to be symmetric), with width d. For each d I use I do indeed get a gaussian distribution however the standard deviation, sigma, depends on my choice of d. The resulting gaussian should have a sigma of roughly 0.701 but I find that the value I actually get depends on the parameter d, when it shouldn't.
I am not sure where the error in this code is, any help would be greatly appreciated!
...ANSWER
Answered 2022-Feb-02 at 20:28You need to save x even when it doesn't change. Otherwise the center values are under-counted, and more so as d increases, which increases the variance.
QUESTION
I am aiming to create a progress bar for an iteration happening inside an installed module.
To create a progress bar for an iteration inside a user-defined function, I pass an tqdm.notebook.tqdm_notebook object as iterable:
ANSWER
Answered 2022-Jan-01 at 21:07Of course generally there is no way to directly modify some existing code you didn't write yourself (whether or not it's "installed" is not the issue).
If you think it's really of general use or interest you could propose a patch to allow this function to take, e.g., a callback function to call on each loop. It might be useful if it's a slow function in general (I did notice some things in the implementation that could be changed to speed it up, but that's another matter).
You could of course find a number of clever hacks to make it work in this one specific case, though it would be fragile considering that it's a hack designed specifically to the implementation details of this function. I found a few possibilities for this.
The simplest seems to be this stupid trick:
Make an ndarray subclass (I called it tqdm_array) which when iterated in Python returns an iterator over a tqdm progress bar which wraps the array itself:
QUESTION
While plotting with a meshgrid defined like this:
...ANSWER
Answered 2021-Dec-09 at 17:33Look at the array shapes:
QUESTION
How can I make the lines for the x- and y-axes thicker in Julia Plots? Is there a simple way to achieve this?
MWE:
...ANSWER
Answered 2021-Nov-28 at 20:42Currently, there does not seem to be an attribute for axes thickness in Plots.jl.
As a workaround, you may use the attribute thickness_scaling, which will scale the thickness of everything: lines, grid lines, axes lines, etc. Since you only want to change the thickness of axes, you need to scale down the others. Here is your example code doing that using pyplot backend.
QUESTION
Suppose the random variable X ∼ N(μ,σ2) distribution
and I am given the observations x =(0.7669, 1.6709, 0.8944, 1.0321, 0.0793, 0.1033, 1.2709, 0.7798, 0.6483, 0.3256)
Given prior distribution μ ∼ N(0, (100)^2) and σ2 ∼ Inverse − Gamma(5/2, 10/2).
I am trying to give a MCMC estimation of the parameter.
Here is my sample code, I am trying to use Random Walk MCMC to do the estimation, but it seems that it does not converge:
...ANSWER
Answered 2021-Nov-11 at 10:55There are a few problems with the code.
You calculate the log-likelihood, but ignore the log-prior. So effectively you're using a uniform prior, not the one you specified.
The initial calculation of the log likelihood has parentheses in the wrong place:
QUESTION
I have a gradient exploding problem which I couldn't solve after trying for several days. I implemented a custom message passing graph neural network in TensorFlow which is used to predict a continuous value from graph data. Each graph is associated with one target value. Each node of a graph is represented by a node attribute vector, and the edges between nodes are represented by an edge attribute vector.
Within a message passing layer, node attributes are updated in a certain way (e.g., by aggregating other node/edge attributes), and these updated node attributes are returned.
Now, I managed to figure out where the gradient problem occurs in my code. I have the below snippet.
...ANSWER
Answered 2021-Oct-29 at 16:33Looks great, as you have already followed most of the solutions to resolve gradient exploding problem. Below is the list of all solutions you can try
Solutions to avoid Gradient Exploding problem
Appropriate Weight initialization: utilise appropriate weight Initialization based on the activation function used.
Initialization Activation Function He ReLU & variants LeCun SELU Glorot Softmax, Logistic, None, TanhRedesigning your Neural network: use fewer layers in neural network and/or use smaller batch size
Choosing Non Saturation activation function: choose the right activation function with reduced learning rates
- ReLU
- Leaky ReLU
- randomized leaky ReLU (RReLU)
- parametric leaky ReLU (PReLU)
- exponential linear unit (ELU)
Batch Normalisation: Ideally using batch normalisation before/after each layer, based on what works best for your dataset.
after each layer Paper reference
QUESTION
I have the following:
...ANSWER
Answered 2021-Oct-20 at 14:08Agda compiles all definitions by pattern matching to a case tree, as described in the user manual page on function definitions. In your case, the case tree looks as follows:
QUESTION
I am still quite new to coding but wanted to learn about modules. I am trying to build an app that will display data based on a selected date range. I tried to do this with different modules for selecting the dates and other modules to handle data and eventually create plots. The goal is to reuse some of these modules in different parts of the app. I seem to have become stuck following the article here. I realise that the article describes the callModule method of using modules.
Here is a sample of the app and modules:
...ANSWER
Answered 2021-Sep-16 at 08:26You were almost there, here's the complete example for reproducibility (fixing some issues with your example):
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