DDoS Attack Detection System Using Semi-supervised Machine Learning in SDN
Bibliographic record
Abstract
Distributed Denial of Service (DDoS) attacks is one of the most dangerous cyber-attack to Software Defined Networks (SDN). It works by sending a large volume of fake network traffic from multiple sources in order to consume the network resources. Among various DDoS attacks, TCP SYN flooding attack is one of the most popular DDoS attacks. In this attack, the attacker sends large amounts of half-open TCP connections on the targeted server in order to exhaust its resources and make it unavailable. SDN architecture separates the control plane and data plane. This separation makes it easier to the controller to program and manage the entire network from single device to make better decisions than when the control is distributed among all the switches. These features will be utilized in this thesis to implement our detection system. Researchers have proposed many solutions to better utilize SDN to detect DDoS attacks, however, it is still a very challenging problem for quick and precise detection of this kind of attacks. In this thesis, we introduce a novel DDoS detection system based on semi-supervised algorithm with Logistic Regression classifier. The algorithm is implemented as a software module on POX SDN controller. We have conducted various test scenarios, comparing it with the traditional approach in the literature. The approach presented in this thesis manages to have a better attack detection rate with a lower reaction time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".