That answer would be B hope it helps
Natural resource systems hope this helps
Answer and Explanation:
Advanced Persistent Threat abbreviated as APT is a type of cyber attack which gives access to the unauthorized user to enter the network without being detected for a long period.
APTs are generally sponsored by the government agencies of the nation or large firms. For example, one of the ATPs used was Stuxnet in the year 2010 against Iran, in order to put off the nuclear program of Iran.
Some of the practical strategies for protection against APT are:
- Sound Internal Auditing
- Strong Password Accessing Policies
- Stringent policies for accessing any device
- Introduction and implementation of multi factor authentication
- Strong IDs and sound honeypot solutions
Answer:
Check the explanation
Explanation:
Here is the program with function definition and two sample calls.
Code:
#include <iostream>
using namespace std;
//checkMe FUNCTION which takes values a, b and c
void checkMe(char &a, int &b, int &c)
{
//if sum of b and c is negative and a is 'n', b and c are set to 0, otherwise a is set to 'p'
if((b+c)<0 && a=='n')
{
b = 0;
c = 0;
}
else
{
a = 'p';
}
}
int main()
{
//first test case when else part is executed
char a = 'n';
int b = 5;
int c = 6;
checkMe(a, b, c);
cout<<a<<" "<<b<<" "<<c<<endl;
//second test case when if part is executed
a = 'n';
b = -4;
c = -5;
checkMe(a, b, c);
cout<<a<<" "<<b<<" "<<c<<endl;
return 0;
}
Kindly check the Output below:
Answer:
a. This is an instance of overfitting.
Explanation:
In data modeling and machine learning practice, data modeling begins with model training whereby the training data is used to train and fit a prediction model. When a trained model performs well on training data and has low accuracy on the test data, then we say say the model is overfitting. This means that the model is memorizing rather Than learning and hence, model fits the data too well, hence, making the model unable to perform well on the test or validation set. A model which underfits will fail to perform well on both the training and validation set.
