Real-time Support Vector Machine based Network Intrusion Detection system using Apache Storm
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
Network intrusion detection is critical component of network management for security, quality of service and other purposes. These systems allow early detection of network intrusion and malicious activities; based on this detection, appropriate actions can be applied to manage these attacks. Several network intrusion detection systems are proposed and evaluated and many of them are currently in use to provide better security. Currently, computer networks are generating high volume of data traffic which cannot be analyzed by most network intrusion detection systems. This situation requires new techniques that can handle huge volume of real time data traffic and it must maintain the high throughput. We have proposed to network intrusion system based on support vector machine in this work. We also propose to use Apache Storm framework; which is a real-time distributed stream processing framework. This network intrusion system is tested for KDD 99 network intrusion dataset.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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