Smart and sustainable threat intelligence
Bibliographic record
Abstract
Distributed Denial of Service (DDoS) attack is known to be the most dangerous attack in cyber-threats which is identically collective in the world of today&s;s interconnected networks, as it mainly disrupts and makes vital services unavailable. Hackers are skilled in using multi- classification DDoS attacks to evade the detection for exploitation of the targeted networks. A proactive and efficient detection mechanism is much needed to secure the networks from multiclass DDoS attacks. However, implementing DDoS detection schemes is very difficult due to the factors such as cost, complexities, and inflexibility. Most of the work has been done to tackle these attacks; still, the open question is to find out an efficient, smart and sustainable model amongst available choices. Therefore, in this research, the author proposes a novel, lightweight Cuda- powered Deep Neural Network Long Short-Term Memory (Cu-DNNLSTM) enabled smart and sustainable threat intelligence system for large and distributed enterprise networks and is simply termed as Multiclass DDoS Detection mechanism (MDDM). The proposed approach is implemented using the current state of the art Canadian Institute of Cybersecurity (CIC)-DDoS2019 dataset that is publicly available and has been thoroughly evaluated. The preliminary results achieves 99.60% detection accuracy with a relatively low ratio of False Positives (FP) (i.e., 0.0003). Additionally, the proposed approach has also been compared with two other DL models to show the promising performance of the proposed approach. Finally, the proposed approach is highly scalable, cost effective, flexible and sustainable to be customized for any emerging computational and communication paradigm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".