Hello, I am Dillon Griffey, and welcome back to my blog. Today, I want to share the culmination of my academic journey and the impact it has had on my career and personal growth.

Lessons Learned

Throughout the program, I gained a deep understanding of advanced computer science concepts and practical skills. The curriculum covered a wide range of topics, including machine learning, database management, and software engineering principles.

One of the key takeaways was the importance of a structured approach to problem-solving. The program emphasized the need to break down complex problems into manageable components, which has been invaluable in my projects like HEDA and KIT-KAT.

Transforming My Approach to Building Software

The Master’s program has significantly influenced my software development practices. Early in the program, my focus was primarily on getting things to work. However, as I progressed, I learned the importance of writing clean, maintainable, and scalable code.

For example, in my Ark Ascended Manager project, I initially faced issues with code complexity and maintainability. Through my coursework, I adopted best practices such as modular design and the use of design patterns, which made my codebase more robust and easier to manage.

Evaluating Software

My approach to evaluating software has also evolved. I now place a greater emphasis on thorough testing and continuous integration. The program taught me the value of automated testing frameworks and the importance of continuous deployment pipelines.

In my AI projects, such a HEDA, I integrated these practices to ensure that the software performs reliably under different conditions. This has reduced the number of bugs and improved the overall user experience.

Comparing Early and Recent Work

Looking back at my early journal entries and classwork, I see a clear progression in my skills and understanding. Initially, my projects were smaller and less complex. As I advanced through the program, my projects became more ambitious, and my approach more systematic.

For instance, my early projects often lacked proper documentation and testing. In contrast, my recent projects, such as the automated response system for HEDA, are well-documented, thoroughly tested, and built with scalability in mind.

Future Plans

With my Master’s degree completed, I am excited to continue exploring the intersection of AI and cybersecurity. My immediate goal is to enhance HEDA by fine-tuning its machine learning models and integrating new security features such as a basic pentest module.

Additionally, I plan to delve deeper into AI research and contribute to open-source projects. I am also considering pursuing certifications in cloud computing and advanced cybersecurity to further expand my expertise.

Tying It All Together

My journey through the Master’s program has been transformative. It has equipped me with the knowledge and skills needed to tackle complex challenges and build innovative solutions. The program has also reinforced the importance of continuous learning and adaptation in the ever-evolving field of computer science.

Thank you for following along on this journey. Stay tuned for more updates and insights as I embark on the next chapter of my career.

Hello, I am Dillon Griffey, and welcome back to my blog. Today, I want to share the progress and challenges I faced in enhancing HEDA's alert system.

Goal for the Month

The primary goal for this month was to implement an api system for HEDA to pull and update information. I also aimed at making the autom response system for malicous packets.

Feature Development

The new api system is designed to pull and update HEDA database information. When a packet is flagged as potentially malicious, a automatic response is generated, a log which includes the packet's source and destination IP addresses, ports, protocol, and payload is generated. Users can then review this information and confirm whether the packet is indeed malicious or a false positive. This then can be queried using the api.

To build this feature, I used several tools and technologies:

• Electron for the desktop application framework
• SQLite for the database to store packet logs and notifications
• Scapy for packet sniffing and analysis
• Python for automatic response and the API back end.
• TensorFlow for machine learning model integration

One significant challenge was creating a token system that the user could generate and use for the api. I had to design a system where the token would remain secure but also allow for the user to easily update/modify the key if needed. Additionally, I ran into issue when making the automatic response - at first the system would write multiple firewall updates about the same IP. I had to design logic to prevent spamming the same IP being blocked.

Visual Components

Retrospective

This month was a mix of successes and challenges. The api is a must have for users to integrate all data moving forward. Additionally having a way to auto block/ and white list specific IP is much needed for this type of application.

What went right:

• Successful inclusiong of a secure api system.
• Automatic Responses on malicously detected packets.

What went wrong:

• VPN would some time trigger the automatic responses.
• The token system was failing intially for new systems that had not a previous token.

Moving forward, I plan to enhance api options and allow for more settings over bearer tokens that are generated.

Additional Insights

Time management has been crucial in balancing feature development and user testing. Collaborating with my advisor provided valuable feedback and direction, helping to refine the feature. The knowledge gained from previous courses in machine learning and database management has been instrumental in this project's progress.

With the api system working and automatic responses working HEDA now just needs to focus on fine tuning and training the models on new or excisting threats. Additionally I plan to integrate a basic pentest of the host system to show exposed ports and outdated software.

Thank you for following along on this journey. Stay tuned for more updates and insights into the development of HEDA.

Hello, I am Dillon Griffey and I want to thank you for visitng my website. Today I want to discuss my journey on developing HEDA, a cybersecurity assistant to help defend, protect and empower users.

The inception of HEDA was driven by the vision to create a user-friendly system that doesn't compromise on security. From the get-go, HEDA was envisioned to be intuitive and accessible, thereby empowering users to take control of their digital security. The user interface is crafted to simplify the complexity of cybersecurity, allowing users from all backgrounds to navigate confidently.

HEDA's proactive approach to cybersecurity leverages real-time threat detection, automated response mechanisms, and behavioral analytics to anticipate and neutralize risks before they escalate. The integration of these advanced technologies within a user-friendly interface epitomizes the next generation of cyber defense systems.

As HEDA continues to evolve, our commitment to enhancing system capabilities and user experience remains steadfast. With an eye towards the future, HEDA aims to incorporate adaptive machine learning models and broaden its cross-platform support, ensuring a secure and consistent user experience across various devices and operating systems.

Stay tuned to this space as we share regular updates on HEDA's development journey, feature releases, and insights into the realm of cybersecurity.

Thank you for reading, and we look forward to having you join us in our quest for a safer digital world.