AI-Enhanced Intrusion Detection System for IoT Edge Networks
Bibliographic record
Abstract
The Internet of Things (IoT) connects billions of smart devices, creating a highly dynamic environment that demands efficient and secure data communication. However, IoT edge networks are highly vulnerable to cyberattacks due to limited resources and diverse communication protocols. To address these challenges, this paper presents an AI-Enhanced Intrusion Detection System (IDS) for IoT edge networks that integrates artificial intelligence with edge computing to achieve real-time threat detection and response. The proposed system employs machine learning and deep learning algorithms to analyze network traffic, detect anomalies, and accurately predict potential intrusions while minimizing false positives. By processing data at the edge, the system ensures low latency, scalability, and energy efficiency, overcoming the limitations of traditional cloud-based IDS solutions. Experimental evaluations demonstrate that the AI-based IDS improves detection accuracy, adapts to evolving attack patterns, and enhances the overall security, reliability, and resilience of IoT infrastructures. This study emphasizes the transformative potential of AI in developing intelligent, adaptive, and future-ready cybersecurity frameworks for next-generation IoT ecosystems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| 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".