Bibliographic record
Abstract
Cyber-attack studies are at the core of cybersecurity studies.Cyber-attacks threaten our ability to use the Internet safely, productively, and creatively worldwide and are the source of many security concerns.However, the "cyberattack" concept is underdeveloped in the academic literature and what is meant by cyber-attack is not clear.To advance theory, design and operate databases to support scholarly research, perform empirical observations, and compare different types of cyber-attacks, it is necessary to first clarify the "concept of cyber-attack".In this thesis, the following research question is addressed: How to represent a cyber-attack?Entity Relationship Diagrams are used to examine definitions of cyber-attacks available in the literature and information on ten successful high-profile attacks that is available on the Internet.This exploratory research contributes a representation and a definition of the concept of cyber-attack.The representation organizes data on cyber-attacks that is publicly available on the Internet into nine data entities, identifies the attributes of each entity, and the relationships between entities.In this representation, Adversary 1 (i.e., attacker) acts to: i) undermine Adversary 2's networks, systems, software or information or ii) damage the physical assets they control.Both adversaries share cyberspace and are affected by factors extrinsic to their organizations.Adversary 2 is comprised of two parts; one includes the organizations that operate the network and the other that is extrinsic to the iii organizations that operate the network.Although this research will be of interest to a broad community, it will be of particular interest to senior executives, government contractors, and researchers interested in contributing to the development of an interdisciplinary and global theory of cybersecurity.has been a tremendous mentor for me.You have done beyond a supervisor's responsibility.It has been such a great honour to be your student.I can write a dissertation on how a wonderful human being you are.This thesis would not have been possible without the guidance and the help of Professor Bailetti.Thank you for being best possible role model I could have hoped for.Working with you has been a most rewarding moment of my life.For your patience, kindness, advice and devotion, thank you.I would like to express my sincere appreciation to Dan Craigen for his generous sharing of his unique knowledge.I will be forever grateful to him for the many ways he contributed to this thesis.A special thank to my family, which this journey would not have been possible without the support of them.Words cannot express how grateful I am to my father, Mohammad Hassan, whom has 2 PhDs, which was the biggest motivation for me to do my master's.I understood the real meaning of love when you said that you were proud of me even when I failed.Thank you for working hard to provide for our family.I owe my deepest gratitude to my mother
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".