Project Proposal

Project Proposal Introduction: In the realm of cybersecurity, understanding network traffic patterns is crucial for detecting threats and optimizing network performance. This project aims to conduct a comparative analysis of different machine learning models’ effectiveness in analyzing various types of network traffic data, including connectivity logs (CONN), metadata of transmitted files (Files), DNS, HTTP, and SSL. Additionally, we will explore whether a single model stands as the standalone best solution for traffic analysis or if a combination of models yields superior results. Moreover, we will detail how applications can leverage this information without the need to develop the application itself. Research Objectives:

• Determine the effectiveness of different machine learning models in analyzing CONN, Files, DNS, HTTP, and SSL traffic data.
• Investigate whether a single model demonstrates superiority in traffic analysis across different data types or if a combination of models is more effective.
• Detail how applications can utilize the findings from our analysis without the necessity of building the application.

Methodology:

• Data Collection and Preprocessing: Assemble diverse, labeled datasets from a public source [1] containing CONN, Files, DNS, HTTP, and SSL traffic data. Preprocess the data to manage missing values, normalize features, and ensure uniformity across datasets.
• Experimental Design: Enumerate all possible permutations of our traffic type, feature selection, model classifier, and training type sets. Employ supervised classification machine learning techniques to evaluate results.
• Model Evaluation: Evaluate model performance of each permutation, and generate comparative graphs using confusion matrices, F-measure, accuracy, and other result metrics.
• Comparative Analysis: Compare the performance of different models across all data types together and individually for CONN, Files, DNS, HTTP, and SSL traffic. Explore implications for real-world applications.
• Application Implications: Detail how applications can utilize the research findings to enhance network security and optimization without developing the application itself.

Expected Outcomes:

• Identification of the most effective machine learning models for analyzing different types of network traffic data.
• Insights into whether a single model or a combination of models is the best solution for traffic analysis.
• Recommendations for applications to leverage research findings in network security and optimization without the need for application development.

References

• [1] Bendl, Š., Valeros, V., & Garcia, S., 2023, “CTU-SME-11: A Labeled Dataset with Real Benign and Malicious Network Traffic Mimicking a Small Medium-Size Enterprise Environment”, Zenodo, https://doi.org/10.5281/zenodo.7958259

ADDITIONAL INSTRUCTIONS FOR THE CLASS – Project Proposal

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• Discussion Questions (DQ)

Initial responses to the DQ should address all components of the questions asked, including a minimum of one scholarly source, and be at least 250 words. Successful responses are substantive (i.e., add something new to the discussion, engage others in the discussion, well-developed idea) and include at least one scholarly source. One or two-sentence responses, simple statements of agreement or “good post,” and responses that are off-topic will not count as substantive. Substantive responses should be at least 150 words. I encourage you to incorporate the readings from the week (as applicable) into your responses.

• Weekly Participation

Your initial responses to the mandatory DQ do not count toward participation and are graded separately. In addition to the DQ responses, you must post at least one reply to peers (or me) on three separate days, for a total of three replies. Participation posts do not require a scholarly source/citation (unless you cite someone else’s work). Part of your weekly participation includes viewing the weekly announcement and attesting to watching it in the comments. These announcements are made to ensure you understand everything that is due during the week.

• APA Format and Writing Quality

Familiarize yourself with the APA format and practice using it correctly. It is used for most writing assignments for your degree. Visit the Writing Center in the Student Success Center, under the Resources tab in Loud-cloud for APA paper templates, citation examples, tips, etc. Points will be deducted for poor use of APA format or absence of APA format (if required). Cite all sources of information! When in doubt, cite the source. Paraphrasing also requires a citation. I highly recommend using the APA Publication Manual, 6th edition.

• Use of Direct Quotes

I discourage over-utilization of direct quotes in DQs and assignments at the Master’s level and deduct points accordingly. As Masters’ level students, it is important that you be able to critically analyze and interpret information from journal articles and other resources. Simply restating someone else’s words does not demonstrate an understanding of the content or critical analysis of the content. It is best to paraphrase content and cite your source.

• LopesWrite Policy

For assignments that need to be submitted to Lopes Write, please be sure you have received your report and Similarity Index (SI) percentage BEFORE you do a “final submit” to me. Once you have received your report, please review it. This report will show you grammatical, punctuation, and spelling errors that can easily be fixed. Take the extra few minutes to review instead of getting counted off for these mistakes. Review your similarities. Did you forget to cite something? Did you not paraphrase well enough? Is your paper made up of someone else’s thoughts more than your own? Visit the Writing Center in the Student Success Center, under the Resources tab in Loud-cloud for tips on improving your paper and SI score. Project Proposal

• Late Policy

The university’s policy on late assignments is a 10% penalty PER DAY LATE. This also applies to late DQ replies. Please communicate with me if you anticipate having to submit an assignment late. I am happy to be flexible, with advance notice. We may be able to work out an extension based on extenuating circumstances. If you do not communicate with me before submitting an assignment late, the GCU late policy will be in effect. I do not accept assignments that are two or more weeks late unless we have worked out an extension. As per policy, no assignments are accepted after the last day of class. Any assignment submitted after midnight on the last day of class will not be accepted for grading.

• Communication

Communication is so very important. There are multiple ways to communicate with me: Questions to Instructor Forum: This is a great place to ask course content or assignment questions. If you have a question, there is a good chance one of your peers does as well. This is a public forum for the class. Individual Forum: This is a private forum to ask me questions or send me messages. This will be checked at least once every 24 hours.

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