Awareness model for minimizing the effects of social engineering attacks in web applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social Engineering (SE) Attacks against information systems continue to pose a potentially devastating impact. Security information systems are becoming increasingly significant as the number of SE incidents rapidly increased and became more aggressive than before. The World Wide Web (WWW) has evolved for information exchange and knowledge-sharing. It enables the sharing of information in a timely, effective, and transparent manner. Identity theft and identity misuse are two sides of cybercrime in which hackers and fraudulent users collect sensitive information from current legal users in order to perform fraud or deceit for financial gain. Malicious links are used as phishing methods, in which malicious links are planted beneath legitimate-looking links. As the number of web pages grows, the number of malicious web pages and the attacks of such become more complex. In this paper, we provide a method for identifying malicious web pages using a crawling and classification approach that helps to support the automatic discovery of the malicious links. The proposed approach can successfully complete the crawling session even if the page requires partial page refreshment and authentication credentials. The evaluation of the proposed approach shows a higher accuracy compared to an existing approach with an overall accuracy of 72% in three custom applications. Moreover, the proposed approach will calculate the significance and the impact severances of each link on the website and it better differentiates malicious web pages and normal links. The results of the proposed approach will also help in providing a set of recommendations which can increase the awareness level of the end-users, website administrators on how to better deal with these types of SE 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it