Game-Theory-Based Active Defense for Intrusion Detection in Cyber-Physical Embedded 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
Cyber-Physical Embedded Systems (CPESs) are distributed embedded systems integrated with various actuators and sensors. When it comes to the issue of CPES security, the most significant problem is the security of Embedded Sensor Networks (ESNs). With the continuous growth of ESNs, the security of transferring data from sensors to their destinations has become an important research area. Due to the limitations in power, storage, and processing capabilities, existing security mechanisms for wired or wireless networks cannot apply directly to ESNs. Meanwhile, ESNs are likely to be attacked by different kinds of attacks in industrial scenarios. Therefore, there is a need to develop new techniques or modify the current security mechanisms to overcome these problems. In this article, we focus on Intrusion Detection (ID) techniques and propose a new attack-defense game model to detect malicious nodes using a repeated game approach. As a direct consequence of the game model, attackers and defenders make different strategies to achieve optimal payoffs. Importantly, error detection and missing detection are taken into consideration in Intrusion Detection Systems (IDSs), where a game tree model is introduced to solve this problem. In addition, we analyze and prove the existence of pure Nash equilibrium and mixed Nash equilibrium. Simulations show that the proposed model can both reduce energy consumption by up to 50% compared with the existing All Monitor (AM) model and improve the detection rate by up to 10% to 15% compared with the existing Cluster Head (CH) monitor model.
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 | 0.001 | 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