Security of SDN in an Adversarial Setting: The DDoS Case
Bibliographic record
Abstract
In Software Defined Networking (SDN) centralization of network control creates a single point of failure and a valuable target for threat actors that wish to produce an impact on the network. Notably, network controllers can be targeted by effects such as Denial of Service (DoS) attacks that would cripple the performance of the network as a whole by making critical services unavailable if they are not detected and prevented. In Artificial Intelligence (AI), Machine Learning (ML) techniques are used to identify the presence of malicious distributed DoS activity within a sample of data collected from network activity over an interval of time. However, machine learning techniques are themselves vulnerable to attacks. We perform a detailed security analysis of a highly realistic threat model and experimentally demonstrate the effectiveness of poisoning and evasion attacks on the SDN. After this adversarial AI experiment, we propose a defense mechanism adapted to the machine learning algorithm and able to protect against those attacks on the system. This research highlights the implications of the poor management of the use of AI as a new technology in this field.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".