Gone 'Phishing': This Was the Summer When Consumers Were Lured to Fake Bank Websites with a View to Defrauding Them
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
the online equivalent of the burglar who poses as a gas company employee. It's' phishing, and it's coming to a bank near you. An attorney in the Federal Trade Commission told ABABJ he first heard of back in 1997, but he's well ahead of the general population. word can't be found on dictionary.com and the lack of response from over half a dozen financial industry consultants and lawyers contacted for this story indicates how unfamiliar the industry is with the latest twist in identity theft, which is the FTC's biggest and fastest-growing consumer complaint. Phishing involves stealing corporations' identities as a means to impersonating individuals. Bank of America appears to have been the first U.S. bank phishing target in mid-May following attacks on other online industries, foreign banks, and domestic card-issuers, such as Morgan Stanley's Discover. A rash of bank phishing incidents developed mid-August to mid-September, as two U.S., two Canadian and two U.K. banks reported occurrences. Phishing, derived from fishing for typically involves criminals posing online as corporations requesting customer information. Legitimate-looking spare e-mail often directs consumers to websites, replete with official company logos, artwork, etc., that request personal information. If details including Social Security and bank account numbers are provided the identity thief can draw on the unwitting consumer's credit. Two other little publicized types of institutional impersonation also surfaced since summer. Mid-September, the FTC alerted the public to fake invites to get their credit report free--after providing information that extends even to their bank-card PINs. Late July the FDIC warned that 50 U.S. community banks had been affected by fake newspaper ads, taken in their name, ostensibly to offer loans. not known how many unwitting consumers sent the required cash fee or information, including faxed copies of their driver's license. Those cons were operating out of Canada. Banks subjected to phishing have also discovered themselves to be dealing with international crime rings. Three U.S. banks have gone public about their phishing experiences, but some unlikely reports suggest almost a dozen have been affected. public nature of phishing makes it a high profile, if actually little used means of identity theft, notes Derails Behrman, an analyst with International Data Corp. Behrman sees phishing as a difficult way to steal identities, especially in the U.S. where ubiquitous personal information online makes it easy to open an account in another's name. Edward Schreiber, chief risk officer, of Banknorth Group Inc., the only bank target that agreed to an interview, reckons phishing may be mushrooming among banks now because, It's easier than a phone scam--a popular wav to trick customers out of information a few years ago. The Internet reaches a mass audience very quickly, he adds. Better, if all goes well, the phishing victim does the thief's work by typing in most sensitive details to his cache! Others say bank systems are so fortified it's easier to elicit the customer's unwitting co-operation than to try and hack in. …
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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