Bibliographic record
Abstract
A malicious URL or website is a type of threat that can affect the users' cybersecurity.It can host unsolicited content and lure users into clicking on links and downloading malware.It can also lead to the theft of private information and monetary losses.People must take the necessary steps to prevent these types of threats from happening promptly.Unfortunately, denylists are not capable of identifying new malicious content.Instead, they are mainly used to identify existing threats.Due to the increasing number of studies being conducted on the use of machine learning techniques, the general capabilities of these tools have been improved.The rise of the internet has made it an essential component of our lives.It allows us to exchange information and knowledge in a timelier manner.Unfortunately, identity fraud and identity theft are two of the most common forms of cybercrime.In both cases, the attackers' goal is to collect the users' personal data so they can commit fraud or deceit for financial gain.Phishing, drive-by exploits, and spam are some types of content commonly featured in malicious URLs.They are also designed to trick users into clicking on links and downloading malware.The vast majority of these scams are carried out through email, and they result in losses of billions of dollars.Systems that are capable of quickly identifying and preventing these types of crimes need to be developed, as well as have the ability to spot new malicious content.Blacklist methods have traditionally been used to detect these types of crimes.On the other hand, blacklists cannot identify newly produced harmful content.Due to the increasing number of studies being conducted on machine learning techniques to improve the detection of harmful web pages, the focus on this field has increased.This article presents an algorithm that can analyze and predict the likelihood of a link being good or bad.It is compared with other standard methods to analyze the performance of this method.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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".