Multi-level host-based intrusion detection system for Internet of things
Bibliographic record
Abstract
Abstract The growth of the Internet of things (IoT) has ushered in a new area of inter-connectivity and innovation in the home. Many devices, once separate, can now be interacted with remotely, improving efficiency and organization. This, however, comes at the cost of rising security vulnerabilities. Vendors are competing to create and release quickly innovative connected objects, without focusing on the security issues. As a consequence, attacks involving smart devices, or targeting them, are proliferating, creating threats to user’s privacy and even their physical security. Additionally, the heterogeneous technologies involved in IoT make attempts to develop protection on smart devices much harder. Most of the intrusion detection systems developed for those platforms are based on network activity. However, on many systems, intrusions cannot easily or reliably be detected from network traces. We propose a novel host-based automated framework for intrusion detection. Our work combines user space and kernel space information and machine learning techniques to detect various kinds of intrusions in smart devices. Our solution use tracing techniques to automatically get devices behavior, process this data into numeric arrays to train several machine learning algorithms, and raise alerts whenever an intrusion is found. We implemented several machine learning algorithms, including deep learning ones, to achieve high detection capabilities, while adding little overhead on the monitored devices. We tested our solution within a realistic home automation system with actual threats.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".