Harris Corner Detection Algorithm based Long Short-Term Memory for Detecting Distributed Denial of Service Anomalies in Software Defined Networks
Bibliographic record
Abstract
Software-Defined Networking (SDN) is the innovative development in network technology with a number of attractive features, including management and flexibility. Despite these advantages, SDN was compromised by Distributed Denial of Service (DDoS) attacks, which causes substantial challenge due to the harm networks. Hence, innovative model is required to identify DDoS attacks despite a range of security methods. In order to detect DDoS attacks in SDN in real time, the Harris Corner Detection Algorithm-Long Short-Term Memory (HCDA-LSTM) is presented in this research. The Canadian Institute of Cybersecurity Intrusion Detection system (CICIDS) 2017 dataset is initially used to collect the data. Consequently, the proposed HCDA-LSTM model significantly reduced the challenges of network security, particularly in relation to SDN. The proposed method enhances SDN security by mitigating DoS/DDoS attacks and improve the performance effectively in results. When compared to the existing models such as Deep Learning Approach for Detecting Controller (DLADSC), Extreme Gradient Boosting (XGBoost) and XRDI the suggested HCDA-LSTM method achieved better performances in terms Accuracy in CICIDS 2017 dataset of 99.92%, Precision of 99.64%, F-measure of 98.89% and recall of 98.65% which is comparatively higher when compared to existing models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".