Exploit the vulnerable program (vulnerable.c) to obtain a shell. The vulnerable program and sample exploit (you need to edit the exploit to make it work) are in the Assignments folder which in turn is in the Documents folder in the TritonApps lab environment. Provide commands and the screenshots of the outputs to illustrate your exploit. [10 pts]

Answer the following questions related to the exploit:

a. Which function and statement in the program is the major cause of the vulnerability? Why? [10 pts]

b. What address are you using to overwrite the return address? How did you obtain this address? [10 pts]

c. Draw a figure of the overflow string that leads to a successful buffer overflow attack and a shell. The figure should highlight the important addresses and contents. [10 pts]

d. What offset worked for your exploit? How did you find the offset? [10 pts]

InfoTech in a Global Economy (ITS 832)

Find a peer-reviewed scholarly journal article discussing electronic innovation and the government. Complete a review of the article by writing a 3 pages overview of the article. This will be a detailed summary of the journal article, including concepts discussed and findings. Additionally, find one other source (it does not have to be a peer-reviewed journal article) that substantiates the findings in the article you are reviewing. 

You should use Google Scholar to find these types of articles ( https://scholar.google.com/ ) Once you find the article, you will read it and write a review of it.  This is considered a research article review.

Your paper should meet these requirements: 

• Be approximately three to four pages in length, not including the required cover page and reference page.
• Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
• Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources.
• Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.

 USE Jupyter Notebook

Given Code: ‘Part of answer’ but incomplete…..

# Importing libraries

import pandas as pd

import numpy as np

from sklearn.neighbors import KNeighborsClassifier

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import train_test_split

from sklearn import metrics





# Loading the dataset

data = pd.read_csv('diabetes.csv')



# Separating features and target

features = data[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']]

target = data['Outcome']



# Splitting training and testing data in the ratio 70 : 30 respectively

x_train, x_test, y_train, y_test = train_test_split(features, target, random_state=0, test_size=0.3)





# KNN model

modelKNN = KNeighborsClassifier()



# Train the model

modelKNN.fit(x_train, y_train)



# Predict test set

y_pred_knn = modelKNN.predict(x_test)



# Print accuracy of the KNN model

print(modelKNN.score(x_test,y_test))



# Print Confusion matrix

print(metrics.confusion_matrix(y_test, y_pred_knn))



# Print Classification Report for determining precision, recall, f1-score and support of the model

print(metrics.classification_report(y_test, y_pred_knn))





# Decision Tree model

modelDT = DecisionTreeClassifier()



# Train the model

modelDT.fit(x_train, y_train)



# Predict test set

y_pred_dt = modelDT.predict(x_test)



# Print accuracy of the Decision Tree model

print(modelDT.score(x_test,y_test))



# Print Confusion matrix

print(metrics.confusion_matrix(y_test, y_pred_dt))



# Print Classification Report for determining precision, recall, f1-score and support of the model

print(metrics.classification_report(y_test, y_pred_dt))

Please do the data cleaning and feature selection…

Please use the given code/layout given below original question….

ORIGINAL QUESTION:

“”for more info check original .ipynb file””

Fill out the code below so that it creates a multi-layer perceptron with a single hidden layer (with 4 nodes) and trains it via back-propagation. Specifically your code should: 

1. Initialize the weights to random values between -1 and 1
2. Perform the feed-forward computation
3. Compute the loss function
4. Calculate the gradients for all the weights via back-propagation
5. Update the weight matrices (using a learning_rate parameter)
6. Execute steps 2-5 for a fixed number of iterations
7. Plot the accuracies and log loss and observe how they change over time

First, initialise the parameters. This means determining the following:

Size of the network

Hidden layers (or a single hidden layer in this example) can be any size. Layers with more neurons are more powerful, but also more likely to overfit, and take longer to train. The output layer size corresponds to the number of classes.

Number of iterations

This parameter determines how many times the network will be updated.

Learning rate

Each time we update the weights, we do so by taking a step into the direction that we calculated will improve the accuracy of the network. The size of that step is determined by the learning rate. Taking small steps will slow the process down, but taking steps that are too large can cause results to vary wildly and not reach a stable optimum.

Next, fill in the code below to train a multi-layer perceptron and see if it correctly classies the input.

in[]# Reshape y
y =

# Initializing weights

W_1 =
W_2 =

# Definining number of iterations and learning rate(‘lr’)
num_iter =

learning rate =

in[]# Creating empty lists for loss values(error) and accuracy
loss_vals, accuracies = [], []

for j in range(num_iter):
# Do a forward pass through the dataset and compute the loss
 

# Decide on intervals and add on the current loss and accuracy to the respective list

# Update the weights

in[]# Plot the loss values and accuracy

Higher number of iterations and lower learning rate seems to improve accuracy

 Compare the six core processes in the software development life cycle (SDLC). What do you believe is the most significant difference is between a traditional approach to system development and an iterative/agile development approach? Why?

• Minimum of 1 post using technologies such as Kaltura, PowerPoint, or any other tool to explain the concepts learned during the week.
• Minimum of 1 scholarly source

Initial Post Instructions

This week we went over the different tools that we have available to analyze data in Excel. Share what you learned from this week, what was most interesting, or even what was the most challenging feature for you. Also share as to how you came up with your solutions in the assignments and the type of analysis you decided to use.