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COMP8325 Applications of Artificial Intelligence for Cyber Security

Task:

Learning Outcomes

By completing this project, you should demonstrate your ability to:

• Understand and detect abnormal patterns in a variety of real-world datasets for cyber security applications and systems

• Perform data pre-processing, data exploration and feature engineering on various types and volumes of data for different cyber security applications

• Perform machine learning mode training and evaluation for cyber security applications.• Understand the security threats to machine learning systems deployed for cyber security

• Analyse the trends of applications of machine learning in the cyber security domain

• Communicate professionally in written and oral form to a range of audiences

Submission

This is a group (of at most 3 students) project. Only one submission is required per group. Your team needs to submit the interim report in iLearn via a Turnitin submission portal before the submission deadline.

Presentation

Your team is required to do a presentation for your project to discuss the data analysis process with machine learning, interpreting your results and findings, and your experience in applying machine learning models in cyber security applications. The group presentation will be submitted via video recording. So, you need to have a group meeting with your teammates online and do the presentation. This needs to be recorded for submission. Each group will present for 10 minutes, and each student is expected to present for around 3 minutes. (To make the video have a reasonable size, we recommend Zoom meeting and recording, like what we did for the lecture recordings).

Final Report

Your team needs to submit a final report discussing the process of conducting the project. Your team needs to submit the final report in iLearn via a Turnitin submission portal before the submission deadline.

Source Code

Given this is a group project, all the project related materials should be managed by a team collaboration platform. Specifically, you are required to use GitHub to manage this project. It’s good that some students have adopted GitHub for their Assignment I.

• The source code (in Python) that can be executed with ease. No GUI is required, but clear README or other documentation should be given. This means that we can repeat your data analysis process and results with little effort.

• If the data analysis tasks are reported in the form of Jupyter Notebook, the amount of the report should be roughly equivalent to what has been requested as above. The related literature review and the reflection should still be submitted as required above.

Project Details

1. conduct analysis on real-world data sets (see Table 1) in various cyber security applications, and

2. review the recent research on the topics related to machine learning and security. In the first task, we leverage publicly available cybersecurity datasets related to a particular application space such as “Web Attack Detection” and “Network Intrusion Detection” to evaluate machine learning models we have developed, and can train machine learning models for specific real-world application deployment. In the second task, you are required to further analyse the research trend in machine learning and cyber security. Your first and second tasks should be aligned with each other. In other words, if you choose to analyse a dataset (as your first task) in “Web Attack Detection” then you are required to review (as your second task) research papers related to “Web Attack Detection”.

Data Analysis Task

You are expected to conduct anomaly detection on at least TWO (at least THREE for a group of 3 students) of the following data sets listed in. It is worth noting that unlike the assignments, we have not performed any pre-processing on these data sets. You could get the basic information about the raw data sets on the webpages offering these data sets. For some data sets, you could also find the information about how these data sets have been used in other machine learning models. While you can learn from others on how to pre-process the data sets, it is important to note that we just intend to make use of the data sets, rather than other information on the webpages.

ou are required to select and download a t least TWO (at least THREE for a group of 3 students) data sets listed in Table 1 based on your preference. Then, you need to perform data pre-processing and feature engineering on the selected data sets to prepare data for the anomaly detection models. Depending on a specific data set, this stage may involve the steps such as missing value handling, duplication removal, categorical/continuous attribute processing, TF-IDF feature extraction, time series feature extraction, subsampling/oversampling, dimension reduction, and class label handling. The whole point of these steps is to produce an appropriate data set for the training and evaluation of detection models. Note that some steps may be repeated during model training and tuning in order to obtain better detection performance. All the pre-processing and feature engineering details should be described in detail and justified reasonably in the reports.

You need to use at least two Anomaly detection methods (discussed in our lectures) and you need to compare the detection performance (e,g., AUC and execution time) for these two specific anomaly detection models and show which is better for a data set. Feature selection and hyperparameter tuning should also be considered for performance improvement. The process of training/testing the models, feature selection, hyperparameter tuning, and model comparison should be reported, with visualization of intermediate or final results. Deep result analysis and critical thinking linking to some ML theoretical aspects are also expected.

Research Task

You are required to conduct a literature review on the research topics related to machine learning and cyber security which have attracted plenty of attention from academia and industry. Note that the application area of your research task should be aligned with that of your data-analysis task. By the literature review, you could analyse the trends of applications of machine learning in the cyber security domain. Find at least five (at least 8 for a group of 3 students) research- based publications (journal articles, conference proceedings or books) for each of the two specific topics: machine learning for cyber security applications, and adversarial machine learning (security issues of machine learning models). The length of this literature review should be at least two-page long in your final report.

Depth is preferred over width, i.e., the literature review should cover a specific single research problem in good detail in a consistent manner, rather than simply list several different items superficially. For instance, the literature review could focus on the specific cyber security applications involved in the above data analysis task. Generally, you need to clearly state what kind of research problems the identified literature aims to address, followed by a concise description of their solutions and approaches. More importantly, it is required to criticise the related existing work by pointing out the limitation those methods or approaches might have, while appreciating their advantages. Finally, you need to analyse the research challenges and suggest the trends of research topics as specific as possible. Although being NOT compulsory, if one can empirically study the related existing work with data, that can be regarded as a plus and extra marks might be awarded (if the final total mark is not full).

COMP8325 Applications of Artificial Intelligence for Cyber Security

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