A Review of Techniques and Policies on Cybersecurity Using Artificial Intelligence and Reinforcement Learning Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cybersecurity is a critical process that safeguards networks, systems, and applications against cyber-attacks, wherein digital information is targeted for unauthorized access, manipulation, or destruction. As attackers continually evolve their tactics, addressing cybersecurity challenges has become paramount, especially in sensitive domains like the military and defense industries. This article delves into the challenges that artificial intelligence (AI) faces in the military domain, specifically focusing on defense applications. We review AI algorithms relevant to defense, examining their potential applications and benefits: much of this study revolves around cybersecurity in defense applications, particularly within cyber-physical systems (CPS). We explore reinforcement learning (RL) and deep RL (DRL) algorithms in CPS, aiming to enhance understanding of the cybersecurity implications in this domain. In this context, we present RL and DRL algorithms employed in cyber-attacks and their potential threats and vulnerabilities. Furthermore, we discuss how RL and DRL algorithms can be effectively leveraged for cyber-attack detection and defense applications, providing usable insights into bolstering CPS cybersecurity. By addressing both technical aspects and ethical considerations, this article offers a comprehensive view of the challenges and opportunities surrounding cybersecurity in defense applications.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it