Defending Against Link Flooding Attacks in Internet of Things: A Bayesian Game Approach
Bibliographic record
Abstract
The link flooding attack (LFA) has emerged as a new category of distributed denial of service (DDoS) attacks in recent years. Along with the massive deployment of low-cost insecure Internet-of-Things (IoT) devices, the fast proliferation of IoT botnets dramatically increases the risk of LFAs. However, how to efficiently defend against LFAs in IoT still remains as an open problem. To overcome this challenge, we model the interaction between an LFA attacker and the network manager as a two-person Bayesian game in this article to precisely characterize the behaviors of both sides. Then, the rational behaviors of the attacker and the optimal strategies of the defender are unveiled by deriving the Bayesian Nash equilibrium (BNE). Inspired by the obtained BNEs, a cost-effective decision framework is proposed for the defender to make defense decisions. Furthermore, we numerically analyze the effect of all the related factors and present feasible suggestions to deter attack motivations fundamentally. Experimental results demonstrate that the proposed method not only consistently outperforms baseline methods in terms of the defender’s utilities under different attack intensities, but also is robust to the changes in important parameters, including the value of benign traffic and the latency of traffic scrubbing.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".