Adaptive Early Packet Filtering for Defending Firewalls Against DoS Attacks
Bibliographic record
Abstract
A major threat to data networks is based on the fact that some traffic can be expensive to classify and filter as it will undergo a longer than average list of filtering rules before being rejected by the default deny rule. An attacker with some information about the access-control list (ACL) deployed at a firewall or an intrusion detection and prevention system (IDS/IPS) can craft packets that will have maximum cost. In this paper, we present a technique that is light weight, traffic-adaptive and can be deployed on top of any filtering mechanism to pre-filter unwanted expensive traffic. The technique utilizes Internet traffic characteristics coupled with a special carefully tuned representation of the policy to generate early defense policies. We use Boolean expressions built as binary decision diagrams (BDD) to represent relaxed versions of the policy that are faster to evaluate. Moreover, it is guaranteed that the technique will not add an overhead that will not be compensated by the gain in filtering time in the underlying filtering method. Evaluation has shown considerable savings to the overall filtering process, thus saving the firewall processing power and increasing overall throughput. Also, the overhead changes according to the traffic behavior, and can be tuned to guarantee its worst case time cost.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".