Term Project description In this project, you have to deploy the web application. There are two part of the web application which are the backend, and the frontend. I have provided the frontend docker image, so you need to update some configuration before you run. The backend part you need to clone the source code […]
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SIT384 Cyber security analytics Distinction Task 8.3D: DBSCAN and its parameters Task description: DBSCAN is a density based algorithm – it assumes clusters for dense regions. It doesn’t require that every point be assigned to a cluster and hence doesn’t partition the data, but instead extracts the ‘dense’ clusters and leaves sparse background classified as
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SIT384 Cyber security analytics Credit Task 8.2C: DGA domain name clustering Task description: According to Wikipedia, Domain generation algorithms (DGA) are algorithms seen in various families of malware that are used to periodically generate a large number of domain names that can be used as rendezvous points with their command and control servers. The large
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SIT384 Cyber security analytics Credit Task 8.2C: DGA domain name clustering Task description: According to Wikipedia, Domain generation algorithms (DGA) are algorithms seen in various families of malware that are used to periodically generate a large number of domain names that can be used as rendezvous points with their command and control servers. The large
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SIT384 Cyber security analytics Credit Task 9.1C: Grid Search with Cross-Validation Task description: C and Gamma are the parameters for a nonlinear support vector machine (SVM). The goal of SVM is to find a hyperplane that would leave the widest possible “cushion” between input points from two classes. There is a tradeoff between “narrow cushion,
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SIT384 Cyber security analytics Pass Task 7.1P: K-Means and Hierarchical Clustering Task description: In machine learning, clustering is used for analyzing and grouping data which does not include pre- labeled class or even a class attribute at all. K-Means clustering and hierarchical clustering are all unsupervised learning algorithms. K- means is a collection of objects
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Assignment 3 Due Date & Time: Maximum Weight: Submission: Assignment Type: KXO151 Programming & Problem Solving AIEN-SOU – 2019 10pm (Shanghai) Friday, Week 14, 31 May, 2019 30% (of the total assessment for KXO151) Via MyLO NOTE: All assignments will be checked for plagiarism by a specialist Java program that checks your assignment against other
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Sample Output of Assignment 3, KXO151, 2019 (Note: text in bold are sample inputs from a user) C:\kxo151> javac Asst3.java C:\kxo151> java Asst3 Stalk a Celebrity! ================== You have to find and seduce your ideal celebrity. Now. Would you like to play Stalk a Celeb? n 0 moves. C:\kxo151> java Asst3 Stalk a Celebrity! ==================
IFN501 Assessment 2 – Deliverable 2 (Individual) Project (Programming) – Total Weighting 30% Online Submission of Code (25%) Due Date: Sunday 26th May 2019 by 11.59 pm (Week 12) Code Demonstration (5%) Done during your Practical Sessions in Week 13 Note 1 You are advised to use only the C# concepts taught in this Unit
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Introduction to React, Part 2 This practical follows on from the previous introduction to React. You will need to have completed the exercises in that sheet before you can work your way through this one. You will need the knowledge from that prac as a foundation for the new concepts considered here, and you will
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