DDoS Defense for IoT: A Stackelberg Game Model-Enabled Collaborative Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The proliferation of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) not only threatens the security of digital devices and infrastructure but also severely degrades IoT system performance due to the overly consumed network resources. With the knowledge of identity information of devices and signaling data, Internet service providers (ISPs) can detect and block DDoS traffic by monitoring the upstream IoT packets, and thereby, improve network efficiency. However, inspecting all data packets online for DDoS detection will significantly increase both the network delay and the computational overhead. Therefore, the packet sampling strategy is crucial for the defenders to detect DDoS attacks. To this end, this article formulates a Stackelberg game model to analyze the collaborative IoT packet sampling against DDoS attacks. Through the equilibrium analysis of the DDoS game, we derive the lower bound of packet sampling rate (PSR) that can effectively deter potential attackers. Unlike traditional offline detection, our proposed packet sampling strategy can support both the online detection and proactive prevention of DDoS traffic. As a use case, a multipoint DDoS defense framework is developed to address the IP spoofing in 5G networks based on the proposed packet sampling strategy, which deters DDoS attacks and reduces the packet sampling cost, and thereby, maximizes the IoT utility, compared with existing methods. In typical reflection attacks (in which no more than five packets of response are triggered by a request packet), our proposed scheme not only reduces more than 70% of the sampling rate but also demonstrates superior robustness against boundary condition variation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it