A Review of Techniques and Policies on Cybersecurity Using Artificial Intelligence and Reinforcement Learning Algorithms
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".