PhishTester: Automatic Testing of Phishing Attacks
Bibliographic record
Abstract
Phishing is a web-based attack where users are allured to visit fake websites and provide their personal information. Traditional anti-phishing tools are successful to mitigate the attack partially. Most of the tools are focused on protecting users. However, there exists lack of efforts to help anti-phishing professionals who manually verify a reported phishing site and take further actions. Moreover, current tools cannot detect phishing attacks that leverage vulnerabilities in trusted web applications such as cross site scripting. An attacker might generate input forms by injecting script code and steal credentials. This paper attempts to address these issues by leveraging traditional web application testing method which can be seen as a complementary effort to current anti-phishing techniques. We consider a suspected website as a web application and test the application based on a behavior model. The model is described using the notion of Finite State Machine (FSM) that captures submission of forms with fake inputs and corresponding responses. We then identify several heuristic coverage criteria to detect inconsistencies which lead to the conclusion that a website is phishing or real. We implement a tool named Phish Tester to automate the testing process. We evaluate the proposed approach with both phishing and real applications. The initial results show that the approach incurs negligible false negatives (less than 3%) and zero false positive for detecting phishing and real websites, respectively. The approach can be complementary to current anti-phishing tools to discover advanced phishing attacks.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".