Security threat assessment of simultaneous multiple Denial-of-Service attacks in IEEE 802.22 Cognitive Radio networks
Bibliographic record
Abstract
The Cognitive Radio (CR) is a fully-reconfigurable wireless device that can intelligently sense, manage, and exploit temporarily-vacant licensed spectrum bands during the absence of incumbent users. Broadly, the IEEE 802.22 is the first complete Wireless Regional Area Network (WRAN) standard that utilizes CR technology in the opportunistic access of white spaces in the television (TV) bands. Intrinsically, the increased CR networks' vulnerabilities alongside the experienced growth of attackers' capabilities, creates a strain on CR network designers to act accordingly. However, most of the existing efforts solely examined the issues of Denial-of-Service (DoS) attacks of IEEE 802.22 networks, such as Primary User Emulation (PUE) attack and Spectrum Sensing Data Falsification (SSDF) attack, each treated in isolation. One main challenge in CR network security domain is to efficiently represent possible simultaneous multiple security threats, and assess their effects. Unlike the previous works, this paper addresses the aforementioned challenge through using the holistic approach of assessing the combined effect of simultaneous multiple DoS attacks. The Bayesian Attack Graph (BAG) model is utilized in this paper to capture the probabilistic dependencies among IEEE 802.22 DoS threat-environment and known vulnerabilities. The simulation results indicate up to 51.3% increase in the probability of DoS in IEEE 802.22 networks considering simultaneous multiple attacks in comparison to the most severe sole attack. Finally, the paper introduces the BAG model as a feasible CR vulnerability metric that can facilitate the creation of a security tightening plan.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".