Empowering Security and Trust in 5G and Beyond: A Deep Reinforcement Learning Approach
Bibliographic record
Abstract
Recent advances in 5G and beyond have further expanded the potential of IoT applications, bringing unprecedented levels of connectivity, speed, and low latency. However, these advances come with significant security threats that can cause widespread damage. An effective approach to addressing these issues involves the integration of cutting-edge technologies like machine learning (ML), particularly deep reinforcement learning (DRL). DRL is a specialized area of ML that integrates the concepts of deep learning and reinforcement learning to create effective solutions for various tasks. In particular, DRL can facilitate the creation of intelligent security systems that can adapt to dynamic and intricate IoT applications connected to 5G and beyond networks. However, effectively implementing DRL-based intrusion detection frameworks in IoT applications connected to 5G networks poses significant challenges due to bandwidth utilization and device behavior. The data generated by IoT devices is often limited, and malicious behavior may be infrequent, making it difficult to accurately identify and train the algorithm to detect such behavior. Moreover, DRL algorithms pose a significant challenge for IoT devices constrained by limited bandwidth, as communicating large amounts of data required by DRL algorithms can cause network congestion and delay critical communications. In this article, we introduce a novel approach to improving the security of IoT applications in the 5G and beyond era by developing an intrusion detection system that employs DRL algorithms. Our approach involves a distributed Q-learning algorithm that observes the behavior of connected devices and predicts anomalous actions. Additionally, to overcome the challenges associated with bandwidth utilization and device behavior, we introduce a bandwidth allocation problem based on a reputation mechanism that allocates bandwidth to only trustworthy devices. Finally, we evaluate our proposed intrusion detection system on the selected indicators. The numerical results demonstrate that our proposed approach outperforms the referenced solutions on the selected indicators.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".