Reference no: EM132400239

CSG2341 Intelligent Systems Assignment Help and Solution - Edith Cowan University, Australia

Title - BFS-NB Hybrid Model, the secure future?

Aim - To understand the working of BFS-NB Hybrid Model in Intrusion Detection System.

Objective is to do a research on BFS-NB Hybrid Model and fully understand its working, performance and reliability. Using the publicly available data to know about its features implementing, Deep learning algorithm using C/C++ to know the performance.

This assignment should elaborate on the research plan specified in the Assignment-1. You should choose one or more machine learning or deep learning algorithms/architecture, implement that/them and evaluate their performance.

For implementing algorithm(s), you can code using any programming language of your choice (e.g. Python, C/C++, MATLAB) or use tools like WEKA and MATLAB Toolboxes. In Python, you may use PyBrain or PyTorch libraries for machine learning/deep learning.

For any machine learning based object detection/recognition system you need:

Take input from sensor (or download already collected and publicly available data).

Pre-process the data.

Read about features responsible for identification.

Find a method to extract those features.

Train your features for specific user through any machine learning technique.

However, for deep learning, you do not have to separately extract features.

You need to submit a report describing your methodology and results with a Minimum of 6 and maximum 10 pages (without appendix).

You will also need to submit a video of a maximum 10 minutes with a maximum of five slides. Video should include demonstration of your input/output data/sample, implementation code or parameters if tool boxes used, results and your comment on the results.

Deliverables for Assignment Report:

Abstract - Introduce the topic and summarize the findings.

Introduction/Background - Define the problem, mention its significance, introduce basic concepts/terminologies, and mention in short existing approaches. At the end, mention the organisation of the rest of the report.

Proposed Approach/Implementation Details - Describe the proposed approach. Use a block diagram to illustrate the whole process and then describe each section. You may like to include the pseudo code of the algorithm. Also describe the language/software/tools used.

Performance Evaluation - Describe the dataset used for testing. Discuss the results using table and/or plots/figures.

Conclusion - Summarize the findings, mention limitation (if any) and indicate future work.

Contribution of each Team Member - Mention who did what part of the work. Does not have to be splitted. You may like to work together. Mention if there was any non-participating group member. This section along with the presentation and peer review report will be considered for individual marks (especially for non-active members).

References - Include all papers cited within the body of the report.

Appendix - Provide the codes written and/or screen shots of the different steps (in case you are using WEKA, MATLAB, KNIME or other tools/interfaces).

Presentation (in class/video) - Explain the theory of the chosen algorithm, discuss how it was implemented, demonstrate the testing and explain the results.

Attachment:- Intelligent Systems Assignment File.rar