A novel classification and clustering algorithms for intrusion detection system on convolutional neural network
Bibliographic record
Abstract
At present data transmission widely uses wireless network framework for transmitting large volume of data. It generates numerous security problems and privacy issues which laid a way for developing IDS. IDS act as preventive technique in securing computer networks. Previously there are numerous metaheuristic and deep learning algorithms used in IDS for detecting threats. Some are affected by dynamic growth of feature spaces and others are degraded in performance during detection of threats. One fine-grained model for intrusion detection can be developed by selecting accurate features and testing them with the intelligent algorithms. Based on these explorations, in this research IDS is implemented with intelligence from preprocessing to feature classification. At first stage, data preprocessing is done using binning concept to reduce noise. Secondly feature selection is done dynamically using dynamic tree growth algorithm with fire fly optimization techniques. Finally, these features are processed using DTB-FFNN for detecting anomalies perfectly. This DTB-FFNN is evaluated with popular KDD dataset. Our proposed model cable news network (CNN)-classification is compared with existing intelligent techniques: feed forward deep neural network, support vectors machines, decision tree, and CNN-clustering is compared with k-means, density-based spatial clustering of applications with noise (DBSCAN). The experimental outcome proves that dynamic tree based FFNN and CNN-clustering produce higher accuracy than the existing models.
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.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".