Intrusion Detection System for WSN-Based Intelligent Transportation Systems
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 application of Wireless Sensor Networks (WSNs) in Intelligent Transportation Systems (ITSs) has been the topic of extensive research in the last decade. Various aspects of WSNs have been addressed in the context of transportation networks. In particular, the different aspects of security (confidentiality, integrity, and availability) strongly enticed the research devoted to ITSs. However, those efforts concentrated on preventive techniques constituting a first line of defense (like cryptography and authentication), which are effective in inhibiting the diverse malicious attacks. Relatively, little attention has been paid for employing a second line of defense that can detect intrusive behavior after successfully penetrating the first one. The latter line of defense is known as the Intrusion Detection System (IDS). While mature in wired and many types of wireless networks, IDSs are envisaged to have more opportunities in WSNs. In this paper, we study the incorporation of IDSs in WSN-based ITSs. We distinguish the characteristics of ITSs that affect the design of effective security measures and propose a novel IDS based on the WITS architecture (proposed in [1]).
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