Investigation of Moving Target Defense Technique to Prevent Poisoning Attacks in SDN
Bibliographic record
Abstract
The motivation behind Software-Defined Networking (SDN) is to allow services and network capabilities to be managed through a central control point. Moving Target Defense (MTD) introduces a constantly changing environment in order to delay or prevent attacks on a system. For the effective use of MTD, SDN can be used to help confuse the attacker from gathering legitimate information about the network. This paper investigates how SDN can be used for some network based MTD techniques and evaluate the benefits of integrating techniques in SDN and MTD. In the experiment, network assets are kept hidden from inside and outside attackers. Furthermore, the SDN controller is programed to perform IP mutation to keep changing real IP addresses of the underlying hosts by assigning each host a virtual IP address at a configured mutation rate to prevent attackers from stealing the real IP addresses or using fake IP addresses. The paper demonstrates experimental evaluation of the MTD technique using the Ryu controller and mininet. The results show that the MTD technique can be easily integrated into the SDN environment to use virtual IP addresses for hosts to reduce the chance of poisoning attacks.
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".