(Python 3) A honeypot is a trap set on a network to detect and counteract computer hackers. Honeypot data are studied in Kimou et al. (2010) using Markov chains. The authors obtained honeypot data from a central database and observed attacks against four computer ports 80, 135, 139, and 445 over 1 year. The ports are the states of a Markov chain along with a state corresponding to no port is attacked.Let (Xn)n=0 be a SDTMC where Xn represents the port attacked with S = {80, 135, 139, 445, No attack}.

Weekly data are monitored, and the port most often attacked during the week is recorded. The estimated Markov transition matrix for weekly attacks is with initial distributation v=(0,0,0,0,1)

1) Write a Python 3 code, that will be estimating the probability each port is attacked after 100 weeks. Be sure to include the following in your code:

a) Simulate 100 weeks of attacks, and repeat the simulation 5,000 times. Compute the probability that each port is attacked after 100 weeks. Use the print function to print your answers.

b) Create a bar graph to show the probability of attacks at each port after 100 weeks. Be sure to title your graph and label the axes.