Bringing Intelligence to Software Defined Networks: Mitigating DDoS Attacks
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
As one of the most devastating types of Distributed Denial of Service (DDoS) attacks, Domain Name System (DNS) amplification attack represents a big threat and one of the main Internet security problems to nowadays networks. Many protocols that form the Internet infrastructure expose a set of vulnerabilities that can be exploited by attackers to carry out a set of attacks. DNS, one of the most critical elements of the Internet, is among these protocols. It is vulnerable to DDoS attacks mainly because all exchanges in this protocol use User Datagram Protocol (UDP). These attacks are difficult to defeat because attackers spoof the IP address of the victim and flood him with valid DNS responses coming from legitimate DNS servers. In this paper, we propose an efficient and scalable solution, called WisdomSDN, to effectively mitigate DNS amplification attack in the context of software defined networks (SDN). WisdomSDN covers both detection and mitigation of illegitimate DNS requests and responses. WisdomSDN consists of: (1) a novel proactive and stateful scheme (PAS) to perform one-to-one mapping between DNS requests and DNS responses; it operates proactively by sending only legitimate responses, excluding amplified illegitimate DNS responses; (2) a machine learning DDoS detection module to detect, in real-time, illegitimate DNS requests. This module consists of (a) Flow statistics collection scheme (FSC) to gather the features of flows in an efficient and scalable way using sFlow protocol; (b) Entropy calculation scheme (ECS) to measure randomness of network traffic; and (c) Bayes Network based Filtering scheme (BNF) to classify, based on entropy values, illegitimate DNS requests; and (3) DNS Mitigation scheme (DM) to effectively mitigate illegitimate DNS requests. The experimental results show that, compared to state-of-art, WisdomSDN can effectively detect/mitigate DNS amplification attack quickly with high detection rate, less false positive rate, and low overhead making it a promising solution to mitigate DNS amplification attack in a SDN environment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it