The ways in which Artificial Intelligence improves several facets of Cyber Security-A survey
Bibliographic record
Abstract
With the increase in computer use for the last 10 years, it increases the size of cyber-attacks as well. Since most computers are connected to the network, they may suffer cyber-attacks. Because of these cyber threats, Cyber Security is becoming a really important area in the information technology area. Cyber Security is an area which is mainly responsible for dealing with cyber-attacks by hackers. The improvements and advancements in the information technology area allow cyber criminals to do really complicated and dangerous cyber-attacks. Cyber-attack methods that are being used today are much more effective than the ones in the past. Because of that, there is always a war between cyber security specialists and cyber attackers or hackers. Cyber security analysts, on the other hand, try to come up with new solutions to the methods developed by cyber criminals or hackers. The competition between them continuously advances and improves cyber security technologies every day. The improvements can be either in cyber security or cybercrime side. These days, traditional cyber security solutions are enough to fight against cyber-attacks, but can these solutions be further developed? This is the question everyone in the cyber security area is trying to answer. Of course, yes. Thanks to AI, which is one of the most popular technologies in the information technology area, it is used in many areas ranging from e-commerce, advertising, human resource & recruiting, video games, transportation, surveillance systems etc. AI has been used in cyber security over the last years. Although it also has negative effects, we are going to mostly talk about its positive sides in this article. We are going to go deep into how and why AI is used in cyber security, how AI improves and enhances the current traditional cyber security methods. Then, we will also talk about which AI techniques play an important role in the cyber security area.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".