Analisis Serangan Vulnerabilities Terhadap Server Selama Work from Home saat Pandemi Covid-19 sebagai Prosedur Mitigasi
Bibliographic record
Abstract
Since the COVID-19 Pandemic occurred, companies engaged in the retail sector have experienced a decline in the impact of government regulations such as PSBB (Large-Scale Social Restrictions) so that all activities were carried out from home or Work from Home (WFH). to assist companies or agencies with various types of information systems in carrying out their business activities and operations This server is one of the most important in the retail company. The opening of several accesses from the public network (internet) to the local area network (LAN) The security of a LAN network that is accessed from a public network is usually an administrator's problem. Often, the security problems of both the network and the entire application system, as well as the web server, are neglected just to ensure that operational activities run smoothly, and security is only realised after a disaster occurs. Without a good network security and application system, the application of any sophisticated technology will be very dangerous for the company, institution, or organisation itself. So, it takes a security analysis of all activities on the LAN, servers, and other devices to prevent mitigation and to be more aware of server security vulnerabilities. Based on the context of the existing issues, a penetration testing analysis is required. As supporting material, this research also uses guidelines from the CEH (Certified Ethical Hacker) module and the official Acunetix website. The test of this research is aimed at finding the weaknesses of the existing company/institution servers. Among others, quite a lot of weaknesses were found, where each of these weaknesses has a different handling, ports that should be blocked but are opened freely, and access to public IPs that are less important should be closed. The solutions proposed to overcome these problems include: the use of this Acunetix standard can be maintained and continued; testing is much better if carried out more than two times; periodically upgrading SNMP (Simple Network Management Protocol) vulnerable; increasing the level of server security; migration of quality antivirus; and upgrade of expired operating systems.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".