Subject: ITS834 - Emerging Threats & CountermeasuresTopic:For this assignment, review the article:Abomhara, M., & Koien, G.M. (2015). Cyber security and the internet of things: Vulnerabilities, threats, intruders, and attacks. Journal of Cyber Security, 4, 65-88. Doi: 10.13052/jcsm2245-1439.414Address the following:What did the authors investigate, and in general how did they do so?Identify the hypothesis or question being testedSummarize the overall article.Identify the conclusions of the authorsIndicate whether or not you think the data support their conclusions/hypothesisConsider alternative explanations for the resultsProvide any additional comments pertaining to other approaches to testing their hypothesis (logical follow-up studies to build on, confirm or refute the conclusions)The relevance or importance of the studyThe appropriateness of the experimental designWhen you write your evaluation, be brief and concise, this is not meant to be an essay but an objective evaluation that one can read very easily and quickly. Also, you should include a complete reference (title, authors, journal, issue, pages) you turn in your evaluation. This is good practice for your literature review, which you’ll be completing during the dissertation process.Your paper should meet the following requirements:Be approximately four to six 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.

First thing we’re going to do is learn how to create a sample image that will be useful for this lab. We will do this by using the Matlab command ‘checkerboard’.To give you an idea on how this command works, from the matlab command prompt, type the following:imshow(checkerboard(20));It should create the image shown below: (see fig1.png)The 20 in the command is the number of pixels that each box on the checkerboard will represent.Let’s create an image from this matrix that we can use later. In order to do that, we need to use the ‘imwrite’ command.imwrite(checkerboard(20), ‘checkerboard.jpg’, ‘JPEG’);To verify the above worked correctly, let’s read in the newly made image and display it:myImage = imread(‘checkerboard.jpg’);imshow(myImage); Now that we have a nice image to use for our edge detection algorithm, let’s begin to implement it. First thing we want to do is create our x and y gradient filters:Sx = [1 0 -1; 2 0 -2; 1 0 -1];Sy = [1 2 1; 0 0 0; -1 -2 -1];Convolve each of the above filters with the checkerboard image. Before we are able to use the convolve function we must convert our image into a double. We do that by using the ‘double’ command and passing it image variable name, in this case ‘myImage’.sobelXImage = conv2(double(myImage), Sx, 'same');sobelYImage = conv2(double(myImage), Sy, 'same');Let’s go ahead and take a peek at what our images currently look like:figure('Name', 'X gradient');imshow(uint8(sobelXImage));figure('Name', 'Y gradient');imshow(uint8(sobelYImage));Do the gradient images look like you expect them too? Why or why not?What is missing from the above question and how would you fix it?Now that we understand what the gradients are doing and are able to display them properly, let’s continue on with our edge detection. We now need to square each of the X and Y portions, and then take the square root of them to determine the magnitude of our gradients.E(u,v) = sqrt( (Dx(u,v))2 + (Dy(u,v))2 )Enter the appropriate matlab command for the above formula and display the image. You should see something like fig.pngEach of the edges are shown in white.Part BDownload a sample image from the net. Repeat the above steps and display the original image, the X gradient, and the Y gradient on one form/figure (Note: Don’t forget to convert it to black and white first) .After we sum the squares and take the square root we have the magnitude of our edge strength. We may not want to look at every single edge detected. We can limit which edges we want to see by applying a threshold. We can do this by using the following matlab commands:threshold = 50;edgeDetectedImage = (uint8(edgeDetectedImage) > threshold) * 255;Create 4 additional figures each with their own respective image on it (using threshold {50, 100, 150, 200} ). Make sure the titles of the figures reflect their contents.Turn in the final matlab script that you used to accomplish part B. It should display 5 figures