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Record W4391560673 · doi:10.18260/1-2--41731

Mini-projects based Cybersecurity Modules for an Operating System Course using xv6

2024· article· en· W4391560673 on OpenAlexfundno aff
Quamar Niyaz, Jansen Tan, Divya Ravindra, Sidike Paheding, Ahmad Y. Javaid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAir Force Research LaboratoryNational Science FoundationUniversity of ToledoNational Aeronautics and Space AdministrationU.S. Department of Energy
KeywordsCourse (navigation)Computer scienceComputer securityOperating systemSoftware engineeringEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract Cybersecurity continues to be a critical aspect within every computing division, especially in the realm of operating system (OS) development. The OS resides at the lower layer above the hardware in the computing hierarchy. If the layers above the OS are well hardened, a security flaw in the OS will compromise the resources in those higher layers. Although several learning resources and courses are available for OS security, they are taught in advanced UG or graduate-level computer security classes. In this work, we develop cybersecurity educational modules that instructors can adoptin their OS courses to emphasize security in OS while teaching its concepts. The goal of this work is to engage students in learning security aspects in OS, while learning its concepts. It will give students a good understanding of different security concepts and how they are implemented in the OS. Towards this, we develop security educational modules for an OS course that will be available to the instructors for adoption in their courses. These modules are designed to be used in a UG-level OS course. To work on these modules, students should be familiar with C programming and OS concepts taught in the class. The modules are intended to be completed within the course of a semester. To achieve this goal, we organize them into three mini-projects witheach can be completed within a few weeks. We chose xv6 as the platform due to its popularity as an educational OS for developing the modules. To develop the modules, we referred to the recent version of a popular OS textbook for the security concepts. The topics discussed in it include authentication, authorization, cryptography, and distributed system security. We kept our educational modules mostly aligned with these topics except distributed system security. We also included a module for implementing a defense mechanism against buffer-overflow attacks, a famous software vulnerability. We created three mini-projects for these modules, each accompanied by proper documentation and a GitHub repository. Two versions are created for each project, one for a student's assignment available in the repository and another as a solution version for instructors. The first project implements a user authentication system in xv6. Students will implement various specifications such as password structure with encryption and programs such as useradd, passwd, whoami, and login. The implementation guidelines are provided in the documentation, along with skeleton code. The authorization project implements the Unix-style access control system. In this project, students will modify and create various structures and functions within the xv6 kernel. The last project is to build a defense mechanism against buffer-overflow using Address Space Layout Randomization (ASLR). Students are expected to implement a random number generator and modify the executable file loader in xv6. The submission for each project is expected to demonstrate the module behavior comparable to relevant systems present in production grade OS, such as Linux. Related work and detailed design of these modules will be discussed in the final draft.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.085
GPT teacher head0.335
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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