Viruses and Lotus Notes:- Have the Virus Writers Finally Met Their Match? Viruses and Lotus Notes:- Have the Virus Writers Finally Met Their Match?
Bibliographic record
Abstract
Many companies are standardising on Lotus Notes/Domino as a groupware solution. With this move towards a standard groupware product and strategy, the risks from targeted attacks are increased. Exactly how virus resistant is Lotus Notes and what unique threats do you need to be aware of, and more importantly, how do you counter them? This paper aims to answer the above questions and the following questions, and offer advice that can be used in organisations that have or are planning to standardise on Lotus Notes/Domino. Œ Has Lotus Notes reached the critical mass for virus writers to target it as they have the Microsoft Office components (Word, Excel, PowerPoint and Access)? Œ How will the virus writers approach this new vector for infection? Œ What are the threats from existing viruses, and how can these threats be minimised or eradicated in Lotus Notes? Œ What Notes specific threats (LotusScript, Buttons, Stored Forms, etc…) do I need to be aware of and what can be done to help minimise the potential threats? Œ What in-built security can be leveraged to help minimise the risks from virus attack? Œ How can encrypted mail be effectively used without risking viruses sneaking in through the outer-perimeter defences? This paper was written for, and presented at the 1999 Virus Bulletin conference at Vancouver, Canada on September 30 th- October 1 st 1999. I would welcome any constructive feedback on this paper and it’s content. This paper will be updated from time to time.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".