Work-in-Progress: Enabling Secure Programming in C++ & Java through Practice Oriented Modules
Bibliographic record
Abstract
Abstract Nowadays, cyberattack incidents are happening on a daily basis. As a result, the demand for a larger and more challenging workforce is increasing. To handle this demand, academic institutions offer cybersecurity courses and degree programs into their curricula; however, more efforts are needed to address the high demand of the cybersecurity workforce. This work aims to bridge the gap between workforce shortage and the number of qualified graduates to fill the positions. We approach this by introducing cybersecurity concepts at the early stage of undergraduate curricula of computer science and engineering programs. Secure programming is critical as many cybersecurity incidents happen due to software vulnerabilities. However, most UG-level programming courses pay little attention to secure programming practices. As a result, many students graduate with limited knowledge of security vulnerabilities that might plague the developed software. Our goal in this work is to introduce secure programming at introductory level programming courses so that students should be aware of cybersecurity issues and use this security mindset in advanced level courses and projects in their degree programs. To accomplish this goal, we developed intuitive and interactive modules emphasizing secure programming in C++ and Java courses to help students become secure software developers. These modules will be used alongside the coursework to emphasize certain vulnerabilities within the programming environment of a specific language and allow students to learn cybersecurity topics, enforcing a solid foundation and understanding. We developed cybersecurity educational modules for C++ and Java as they are amongst the popular languages and used in introductory programming courses. While designing these modules, we kept in mind that the topics must be relevant to real-world issues in the software industry. We used a variety of resources and benchmarks to ensure the authenticity of our chosen topics, including Common Weakness Enumeration (CWE) and Common Vulnerability and Exposures (CVE). While choosing module topics to develop, we had some restrictions. For example, the topics must be introductory and easy to understand. These modules are geared towards freshman or sophomore-level UG students who have just started programming. The developed security modules have four components: power-point slides, lab description, code template for the lab, and complete solution. The complete solution for each module will be provided to the instructors to check students' work if they adopt the modules in their courses. The modules developed for a C++ programming course include labs on input validation, integer overflow, random number generation, function call with incorrect argument type, and dangling pointers. In Java, we developed lab modules for input validation, integer overflow, null object reference, random number generator, and data encapsulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".