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ML4Cyber
Fallston, Maryland, United States
FA
Preferred learners
  • Anywhere
  • Academic experience
Categories
Market research Competitive analysis Sales strategy Security (cybersecurity and IT security) Software development
Skills
sales strategy agile software development competitive analysis marketing strategies marketing lead generation process improvement social media campaigns python (programming language) sales
Project scope
What is the main goal for this project?

Marketing / Sales:

1. Some areas that we hope to analyze include, but are not limited to, sales cycle length, average sale price, average ROI/customer, social media and marketing campaign suggestions, competitor analysis.

2. Each student will work with the company mentor to brainstorm new cold leads, deciding the leads that we can pursue , make discovery calls to better understand if our product is beneficial to them and every successful sale will be compensated with a commission.

3. Identify possible B2B opportunities.

Programming /AI /Tool improvements:

1.Python programming knowledge to offer suggestions and implement improvements to our ML tool.

2. Agile Software Development opportunity using python programming knowledge.

3. Suggestions for ML process improvement and expansion of tool functionality.

What tasks will learners need to complete to achieve the project goal?

Deliverables will include any of the following if applicable based on the area students worked on: Source code, AI implementation, reports on process followed for lead generation and calls made. Report and suggestions on market strategy and social media campaigns. Reports on competitor analysis and sales strategy.

How will you support learners in completing the project?

Students will connect directly with us for mentorship throughout the project. We will be able to provide answers to any questions they have and support them by reviewing their work and guiding them through the process.

About the company

Our information society is driven by software created by organizations in the private and public sector. These software applications are rarely perfect. They often contain cyber security vulnerabilities (software “bugs”) which when exploited by malicious attackers can lead to dangerous intrusions of computer systems. We have developed a high-reliability, automated approach to analyzing software code for vulnerabilities that is a leap forward in technology when compared to competing commercial solutions. We propose an efficient Machine Learning approach to identify both true positives and false positives with very high accuracy: greater than 90%. This valuable solution reduces the time required to find vulnerabilities by a factor of ten (10) or more, and the results are highly reliable.