ID-SOMGA: A Self Organising Migrating Genetic Algorithm-Based Solution for Intrusion Detection
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
The study examined the detection of attacks against computer networks, which is becoming a harder problem to solve in the field of Network security. A problem with current intrusion detection systems is that they have many false positive and false negative events. Most of the existing Intrusion detection systems implemented depend on rule-based expert systems where new attacks are not detectable. In this study, optimization algorithms were added to intrusion detection system to make them more efficient. Self Organizing Migrating Genetic Algorithm (SOMGA) was integrated into intrusion detection system to obtain a more efficient intrusion detection system called ID-SOMGA. This study provides an equally efficient method to implement an intrusion detection system that returns very low false positives. Due to the complexities involved in security issues, and the implementation of the work, selected values of the network log was used to implement the system in order to reduce some of these complexities. The Self Organizing Migrating Genetic Algorithm – Intrusion Detection System was tested and values of the result were compared with that of an IDS with Genetic Algorithm Intrusion Detection System. In terms of detection rates, ID-SOMGA was found to be slower than an IDS with GA, the false positives in ID-SOMGA was lower than what obtains with genetic algorithm. Both schemes were able to identify new patterns almost in the same way. The ID-SOMGA system that was developed improved the security of systems in networked settings allowing for confidentiality, integrity and availability of system resources.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| 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