Deep Learning-Based Detection of IoT Botnet Attacks: An Exploration of Residual Networks
Bibliographic record
Abstract
Modern enterprises increasingly employ Internet of Things (IoT) devices across various sectors to enhance service provision, with applications spanning from healthcare to academia.However, the widespread adoption of IoT technology introduces significant security vulnerabilities.Particularly, these devices are susceptible to cyber-attacks, notably those orchestrated by botnets.The challenge of addressing this security issue is further compounded by the devices' memory and energy constraints, which limit the implementation of robust security measures.The present study introduces a Deep Learning Techniques (DLT) based approach, termed Detection of Intrusions in IoT using Residual Networks (DIIOTRNs), to preemptively identify IoT botnet attacks.These attacks typically undergo several stages prior to execution, providing an opportunity for early detection.The proposed DIIOTRNs framework integrates Convolution Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) to effectively detect potential threats.The framework was subjected to empirical testing and demonstrated promising results, achieving accuracy levels exceeding 90%.Thus, the DIIOTRNs approach offers a promising solution to the pressing issue of IoT security, particularly in the context of botnet attacks.Further research is warranted to refine and optimize this framework for broad adoption across the IoT landscape.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".