Network Forensics Against Volumetric-Based Distributed Denial of Service Attacks on Cloud and the Edge Computing
Bibliographic record
Abstract
Cyber attacks are increasingly rampant and even damage the reputation of companies, agencies, and services. DDoS attacks have been overgrowing in the last year, which has resulted in substantial losses. Volumetric-based Distributed Denial of Service (DDoS) is a hazardous attack type because it can consume server resources, causing the server to be unable to serve customer requests. The network design consisting of hardware and software becomes the essential capital that is a determinant of the quality of a network in the long term. A firewall is one way to stop the occurrence of DDoS. Forensics and mitigation in this study apply Packet Filtering Firewall and Circuit Level Gateway Firewall against ICMP-Flood DDoS attacks. The research methodology is a simulated experiment on cloud and edge computing networks. Forensics and mitigation in cloud computing are carried out at layer 3, the Internet Protocol layer TCP/IP model, by applying a Packet-Filtering Firewall with a success rate of 64%-69% traffic reduction. In contrast, the success of reducing server resource usage is 73.75%. At the same time, Edge computing is carried out at layer 4, namely the Transport Protocol layer TCP/IP model, by applying a Circuit-Level Gateway Firewall with a success rate of reducing traffic by 55%-98.88%. In comparison, the success of lowering server resource usage is 96% and restoring traffic and paralyzed servers to normal position.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".