Zone-Based Intrusion Detection for Mobile Ad Hoc Networks.
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
(MANETs) assume a cooperative and friendly network environment and do not accommodate security. Therefore, Intrusion Detection Systems (IDSs), serving as the second line of defense for information systems, are indispensable for MANETs with high security requirements. Central to the research described in this dissertation is the proposed two-level nonoverlapping Zone-Based Intrusion Detection System (ZBIDS) which fit the unique requirement of MANETs. First, in the low-level of ZBIDS, I propose an intrusion detection agent model and present a Markov Chain based anomaly detection algorithm. Local and trusted communication activities such as routing table related features are periodically selected and formatted with minimum errors from raw data. A Markov Chain based normal profile is then constructed to capture the temporal dependency among network activities and accommodate the dynamic nature of raw data. A local detection model aggregating abnormal behaviors is constructed to reflect recent subject activities in order to achieve low false positive ratio and high detection ratio. A set of criteria to tune parameters is developed and the performance trade-off is
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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