Phishing Detection using Decision Tree Model
Bibliographic record
Abstract
In the modern days the security is the main concern in this rapidly evolving world with the technology advancement. There are many of the cases which led to huge number of financial losses by common social attacks. These attacks are the one that made technically or to the targeted device. It's in the form of the virus or Trojan or it may be in the form of a normal website link which we also called as the URL (Uniform Resource Locator).These URLs contains the software or the malicious program which takes out the users all the valuable and more secured and private information (or sensitive data) when this URL is entered by the user in his remote machine. This form of attack is known as Phishing. Normally the user will see the web page appearing as a simple and interactive but in behind it is more and more dangerous one. A fraudulent try made by the attacker in order to steal the users data all the private information like we have username, password, and private details like users financial bank account and details of the users credit card. To avoid these attacks there are many advancements in artificial intelligence and machine learning, which have efficient and more compact techniques to find out the fake URLs. A machine learning model made up of decision tree algorithm is developed which will scan and filtes out the common words and learns the specific features and then it will provide the appropriate result.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".