Where can i apply sigma rules and how?
is it in elastic security detection engine or it could be only integrate with Elastalert ?
my goal is to deploy a SIEM architecture based on elk stack that's why i want to add sigma rules to detecte malicious events
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Global clustering coefficient gives an outline of the clustering in the entire network. From theory, this measure can be applied to both undirected and directed networks.
Networx library provides a function average_clustering(g) that calculates global clustering for undirected networks but not for directed ones. Is there a way to implement the global clustering coefficient for directed networks in Python or are there other libraries that do this?
Thanks
I searched for information to no avail, on stack overflow there is another very similar question but got no answer.
We have spark clusters with 100-200 nodes and we plot several metrics of executors, driver
We are not sure what's the best way to create a dashboard at such scale? Visualizing all the 100-200 nodes and executor stats doesn't surface the problem as there is lot of noise. It also slows down the dashboard tremendously
What are some good practices around grafana dashboards?
Visualize using top K
Plot only anomalies? How do we detect anomalies?
How to reduce noise?
How to make the dashboard more performant?
We use prometheus in the backend
I am writing a MATLAB script that uses Deep Determininstic Policy Gradient to control an Active Suspension System (Dynamic System), but I am stuck on updating the actor network. All of the examples and articles I read, use tensorflow libraries like tf.gradients(). However, I need to know exactly how to apply chain rule to compute the deterministic policy gradient shown in the image to implement it in my MATLAB code.
Context: We are two students intending to write a thesis on reverse engineering namespaces using hierarchical agglomerative clustering algorithms. We have a variation of linking methods and other tweaks to the algorithm we want to try out. We will run the algorithm on popular GitHub repositories and compare the created clusters with the originally existent namespaces. Our work will closely follow the works of this paper. In the paper the authors mentions the use of the “precision recall metric” to measure the accuracy of the clustering algorithm. However looking more closely on the metric and its origin, it seems to be dedicated to flat (non-hierarchical) clusters.
Question:
Is there a way to use the precision recall metric to measure the accuracy of a hierarchy of recovered clusters? If not, what other options exists?
i want to cluster some text document to find the document with the same concept. i've done the semantic similarity using Latent Semantic Analysis (LSA), but i confuse which clustering method that i should choose for my purpose .
Thank you
You can use hierarchical clustering. There is a package in R called RClusterpp which is very efficient for hierarchical clustering of large data (it does a parallel computation). Then you can cut the dendrogram tree for different number of cluster within the possible range and check for cluster profiles using cross-tab.