Analyse the trends of applications of Artificial

Assignment Detail:- COMP8325 Applications of Artificial Intelligence for Cyber Security - Macquarie University Assignment - Description Learning Outcome 1: Explain the basic concepts and the limitations of Artificial Intelligence;Detect intrusion in networks and systems by applying tools and techniques revealing abnormal patterns in datasets; and Learning Outcome 2: Analyse the trends of applications of Artificial Intelligence in cyber security- TASK 1: Merits of Entropy in Attack Detection/Diagnostics Consider a server-log dataset hosted at Google Drive- Two attacks happened on a day, both somewhere around 8am and noon- Please answer the following questions: - Identify the exact date and time1- What approach did the attackers use???? There has been significant literature23 discussing how entropy can be used to detect these attacks- To do it effectively, approximation schemes are usually used- You do not have to implement these approximation techniques, but do present an analysis of whether entropy is useful and which com- binations you tried, e-g- src ip, dest ip, src-port, dst-port, etc- Do any reveal anomalies when the two attacks happen???? TASK 2: Web Tracking A typical webpage consists of several web-components, e-g-, JavaScript codes, Flash-content, images, CSS, etc- When a user opens a website in a web browser, the fetched webpage typically generates several other HTTP-S- connections for downloading additional components of the webpage- These components can be downloaded from the website visited by the user -referred to as first-party domain- or downloaded from other third-party domains- Here, we focus on one type of web-component, namely JavaScript codes, which is loaded both from first- and third-party domains- JavaScript programs are widely used by ad networks, content distribution networks -CDNs-, tracking services, analytics platforms, and online social networks -e-g-, Facebook uses them to implement plugins- Figure1illustrates a typical scenario of web tracking via JavaScript codes- Upon fetching a webpage from first-party domains -steps 1 & 2-, the user's web browser interprets the HTML tags and executes JavaScript programs within the HTML script tags- JavaScript code execution enables the web browser to send requests to retrieve additional content from third-party domains -step 3-- Depending on the implemented function- alities, the JavaScript programs can be considered as useful -functional-, e-g-, fetching content from a CDN, or as tracking- In the latter case, when the webpage is completely rendered -step 4-, the JavaScript codes track user's activities on the webpage, write to or read from the cookie database -steps 5 & 6-, or reconstruct user identifiers- Tracking JavaScript programs may also be used to fingerprint user's browser -as well as system- and to transfer private and sensitive information to third-party domains -step 7-- Now, imagine your are given a task to develop a machine-learning based on only class-i-e-, One Class SVM or Positive Unlabelled -PU- Learning, see ref4- technique to differentiate tracking JavaScript codes from functional ones- To this end you are provided with labelled dataset -see COMP8325's iLearn page- containing labelled functional and tracking JavaScript codes- Here may use the code provided at iLearn to do the following tasks- Use Term Frequency - Inverse Document Frequency -TF-IDF- to extract features from functional and tracking JavaScript codes- Develop either One-Class SVM or PU Learning, and a baseline SVM for comparison, to classify the JavaScript codes-Design and conduct experiments to validate and test the efficacy of your developed model:- To report any over- or under-fitting of the models, you may use 60% of the data for testing, 20% for validation, and 20% for the testing-- Report and discuss the parameters of OCSVM or PU Learning model which give your improved results- Attachment:- Artificial Intelligence for Cyber Security-rar

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