An unsupervised approach for detecting DDOS attacks based on traffic-based metrics
Bibliographic record
Abstract
Recently, distributed denial of service (DDoS) attacks have been widely used to compromise computer systems and a lot of free DDoS attacking tools can be easily obtained from the public network. Although many mechanisms were suggested to prevent DDoS attacks, most of them lack in effectiveness and efficiency. Moreover, trace back and prevention for DDoS intrusions are almost impossible because of the distribution and large number of attacking hosts, and the difficulty of identifying their location due to source IP address spoofing. We define in this paper a new traffic-based metrics named IPTraffic by studying the basic principle of DDoS attacks. An outlier detection algorithm based on Gaussian mixture model (GMM) is used to analyze the value of IPTraffic, and then make intrusion decisions according to the outlier detection result. We evaluate our approach on a live networking environment and the experimental results show that the proposed approach not only can detect DDoS attacks effectively but also provide an efficient response to these attacks.
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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 | 0.000 | 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 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".