Answer:
Encapsulating Security Payload (ESP)
Explanation:
Encapsulating Security Payload is also known as ESP, it is a protocol that exists within IPSec, it helps in determining the authentication, integrity and how confidential network pack data / Payload in IPV4 and IPV6 networks are.
ESP supplies messages /Payload encipher, it also helps in authenticating Payload as well as where it originated from in the IPSec protocol suite.
A person can enrich data in Splunk by
- Preparing to know data that is using Splunk to known the required fields in the data.
- One need to think of this as if one is seeing pieces in a puzzle, then one can notice their shapes.
- The next step is that one need to categorize data as a kind of a preamble before the act of aggregation and reporting.
<h3>What is Enriching Your Data?</h3>
Data enrichment is known to be a kind of an augmentation and it is seen as the act or the process of making better an existing information by the use of a supplementing missing or any kind of incomplete data.
<h3> What is a Lookup?</h3>
Data Lookup is known to be the method used to make plenty any information based on rules.
Hence, A person can enrich data in Splunk by
- Preparing to know data that is using Splunk to known the required fields in the data.
- One need to think of this as if one is seeing pieces in a puzzle, then one can notice their shapes.
- The next step is that one need to categorize data as a kind of a preamble before the act of aggregation and reporting.
Learn more about SPLUNK from
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The correct answer is P2P or peer-to-peer servers.
Hope I helped ;)
Answer:
40
Explanation:
Given that:
A neural network with 11 input variables possess;
one hidden layer with three hidden units; &
one output variable
For every input, a variable must go to every node.
Thus, we can calculate the weights of weight with respect to connections to input and hidden layer by using the formula:
= ( inputs + bias) × numbers of nodes
= (11 + 1 ) × 3
= 12 × 3
= 36 weights
Also, For one hidden layer (with 3 nodes) and one output
The entry result for every hidden node will go directly to the output
These results will have weights associated with them before computed in the output node.
Thus; using the formula
= (numbers of nodes + bais) output, we get;
= ( 3+ 1 ) × 1
= 4 weights
weights with respect to input and hidden layer total = 36
weights with respect to hidden and output layer total = 4
Finally, the sum of both weights is = 36 + 4
= 40
If u mean the indent when you’re writing an essay, then it is an indent to show how u separate the paragraphs. Hope this helps.
