Deep Learning-Enabled Threat Intelligence Scheme in the Internet of Things Networks
Bibliographic record
Abstract
With the prevalence of Internet of Things (IoT) systems, there should be a resilient connection between Space, Air, Ground, and Sea (SAGS) networks to offer automated services to end-users and organizations. However, such networks suffer from serious security and safety issues if IoT systems are not protected efficiently. Threat Intelligence (TI) has become a powerful security technique to understand cyber-attacks using artificial intelligence models that can automatically safeguard SAGS networks. In this paper, we propose a new TI scheme based on deep learning techniques that can discover cyber threats from SAGS networks. The proposed scheme contains three modules: a deep pattern extractor, TI-driven detection and TI-attack type identification technique. The deep pattern extractor module is designed to elicit hidden patterns of IoT networks, and its output used as input to the TI-driven detection. TI-attack type identification is used to identify the attack types of malicious patterns to assist in responding to security incidents. The proposed scheme is evaluated on the two datasets of TON-IoT and N-BAIOT. The experimental results prove that the scheme achieves high performances in terms of the detection and false alarm rates compared with other similar techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".