IoT-PRIDS: Leveraging packet representations for intrusion detection in IoT networks
Why this work is in the frame
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Bibliographic record
Abstract
The Internet of Things (IoT) devices have been integrated into almost all everyday applications of human life such as healthcare, transportation and agriculture. This widespread adoption of IoT has opened a large threat landscape to computer networks, leaving security gaps in IoT-enabled networks. These resource-constrained devices lack sufficient security mechanisms and become the weakest link in our in computer networks and jeopardize systems and data. To address this issue, Intrusion Detection Systems (IDS) have been proposed as one of many tools to mitigate IoT related intrusions. While IDS have proven to be a crucial tools for threat detection, their dependence on labeled data and their high computational costs have become obstacles to real life adoption. In this work, we present IoT-PRIDS, a new framework equipped with a host-based anomaly-based intrusion detection system that leverages “packet representations” to understand the typical behavior of devices, focusing on their communications, services, and packet header values. It is a lightweight non-ML model that relies solely on benign network traffic for intrusion detection and offers a practical way for securing IoT environments. Our results show that this model can detect the majority of abnormal flows while keeping false alarms at a minimum and is promising to be used in real-world applications.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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