SIT 384 Data Analytics for Cyber Security Assignment 2Trimester 1, 2017Objectives•To apply skills and knowledge acquired throughout the trimester in classification algorithms and machine learning...

SIT 384 Data Analytics for Cyber Security Assignment 2Trimester 1, 2017Objectives•To apply skills and knowledge acquired throughout the trimester in classification algorithms and machine learning process.•To rationalize the use of machine learning algorithms to effectively and efficiently process ofdatain big size.•To demonstrate ability to use R to perform email classification tasks that are common for corporate security analyst.•To scientifically conduct and document machine learning experiments for analytics purposes.Due Date: 2pm, Friday, May 19, 2017This assignment consists of a report worth 20 marks.Delays caused by student's own computer downtime cannot be accepted as a valid reason for late submission without penalty. Students must plan their work to allow for both scheduled and unscheduled downtime.Submission instructions:You must submit an electronic copy of all your assignment files via Cloud-Deakin. You must include both your report, source codes, necessary data filesand optionally presentation file. Assignments will not be accepted through any other mannerof submission. Students should note that email and paper based submissions will ordinarily be rejected.Special requirements to prove the originality of your work:On-campus students (B and G) are required to demonstrate the execution of your classification programsin R to your tutor in Week 10; Cloud students are required to attach a 3-5minutes Video presentation to demonstrate how your R codes are executed to derive the claimed results.The video should be uploaded to a cloud storage (You can find outhow to upload a video from https://video.deakin.edu.au/.)Failure to do so will result a delayed assessment of your submission.Late submissions: Submissions received after the due date are penalized at a rate of 5% (out of the full mark) per day, no exceptions. Late submission after 5days would be penalized at a rate of 100% out of the full mark. Close of submissions on the due date and each day thereafter for penalties will occur at 05:00 pm Australian Eastern Time (UTC +10 hours). Students outside of Victoria should note that the normal time zone in Victoria is UTC+10 hours. No extension will be granted.It is the student's responsibility to ensure that they understand the submission instructions. If you have ANY difficulties ask the Lecturer/Tutor for assistance (prior to the submission date).Copying, Plagiarism NoticeThis is an individual assignment. You are not permitted to work as a part of a group when writing thisassignment. The University's policy on plagiarism can be viewed online at http://www.deakin.edu.au/students/study-support/referencing/plagiarism
OverviewThe popularity of social media networks, such as Twitter, leads to an increasing number of spamming activities. Researchers employed various machine learning methodsto detect Twitter spams. In this assignment, you are required to classify spam tweets by using provided datasets. The features have been extracted and clearly structured inJSON format. The extracted features can be categorizedinto two groups: user profile-based features and tweet content-based features as summarized in Table 1.The provided training dataset and testing dataset are separately listed in Table 2and Table 3. In testing dataset, we can findthat the ratio of spam to non-spam is 1:1 inDataset1, while the ratio is 1:19 in Dataset 2. In most of previouswork,thetestingdatasets are nearly evenly distributed. However, in real world, there are only around 5% spam tweetsin Twitter, which indicates thattesting Dataset2 simulatesthe real-worldscenario. You are required to classify spam tweets, evaluate the classifiers’ performance and compare the Dataset 1 and Dataset 2 outcomes by conducting experiments.