The DAO Induction Attack: Analysis and Countermeasure
Bibliographic record
Abstract
We study the destination advertisement object (DAO) induction attack, a new attack against Internet Protocol version 6 (IPv6) routing protocol for low-power and lossy networks (RPL), the standard routing protocol for the Internet of Things (IoT). In the DAO induction attack, a compromised node in the network periodically transmits a special control message. Each of these crafted control messages induces many nodes in the network to transmit in response. We show that transmitting these unnecessary messages can significantly increase the power consumption of nodes, hence reduce the lifetime of battery-operated IoT devices. In addition, we show that the attack severely impacts end-to-end latency and packet delivery ratio, two important network performance metrics. For instance, in a network with 50 nodes, our simulation results show that the attack increases the average end-to-end latency and packet loss ratio by 410% and 260%, respectively. To counter the attack, we propose a lightweight solution. We show that our solution imposes no overhead when the network is in its normal operation (i.e., it is not under attack) and can quickly detect the attack even when the network experiences high packet loss rates.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".