Project Feedback Expansion

Key points of feedback and action steps that we recieved from professors for project development

This is what the professors told us all summarized - note these are mostly ideas and things to look out for. Use discretion when taking this feedback.

The feedback centers around several key areas: licensing, detection mechanisms, privacy concerns, technological considerations, and validation of your technology. Let's expand on each of these points:

1. Licensing and Code Compliance

Double Check Licenses: Ensure that all software components, like those from CodeQL, comply with your project's licensing requirements. For example, if using GPL libraries, understand their implications on your proprietary code.
Compliance: Regularly audit code for license compliance and compatibility, especially if mixing open source and proprietary code.

2. Detection Mechanism: False Positives vs. False Negatives

Abnormal Detection: Develop robust algorithms to distinguish between normal and abnormal scenarios accurately. This includes tuning your system to balance false positives (non-threats identified as threats) and false negatives (missing actual threats).
Data Sources: Utilize diverse datasets, including police and TSA data, for training and refining your models.
Expert Consultation: Engage with law enforcement professionals and security experts to understand real-world scenarios and integrate their insights into your model training.

3. Privacy and Ethical Considerations

Children's Data: Be particularly cautious with minors' data due to heightened privacy concerns.
Hardware Security: Implement measures to prevent theft or tampering of hardware.
Data Handling: Develop protocols for sensitive data handling, including misclassification risks (e.g., malware vs. non-malware).

4. Technological Aspects and System Design

Adversarial Systems: Anticipate and prepare for adversarial attacks on your machine learning models.
Email and Network Integration: Consider integrating email alerts for critical detections and explore using MAC addresses for network-related anomaly detection.
Balancing Alert Systems: Implement a tiered alert system that escalates from low to high probability threats, avoiding unnecessary alerts for minor anomalies.

5. Validation and Credibility

DARPA Questions: Address questions such as the Heilmeier Catechism for clarity and direction. These questions force you to articulate objectives, understand current limitations, and assess the impact and risks of your technology.
Testing and Certification: Consider third-party testing for credibility, such as fake weapons testing for your alert systems. Explore certifications that can validate your technology's reliability and effectiveness.
Tracking and Security: Implement tracking mechanisms (like GPS modules) for additional security and data integrity.

6. Further Research and Development

Explore Additional Factors: Continuously research additional factors that can enhance detection accuracy.
Engage with Academic Resources: Utilize academic connections, such as consulting with professors, to gain insights and potential code contributions (e.g., for MAC address analysis).

7. Future Prospects and Impact Assessment

Potential Impact: Assess the potential societal impact of your technology and how it can improve current practices.
Cost and Timeline Evaluation: Develop a realistic budget and timeline for your project, considering both development and implementation phases.

Ethical Framework: Develop a robust ethical framework to guide decision-making, especially concerning privacy and data security.
Community Engagement: Engage with the community and stakeholders to understand their concerns and incorporate their feedback into your system's development and deployment.