Real-Time Large-Scale Intrusion Detection and Prevention System (IDPS) CICIoT Dataset Traffic Assessment Based on Deep Learning
Bibliographic record
Abstract
This research utilizes machine learning (ML), and especially deep learning (DL), techniques for efficient feature extraction of intrusion attacks. We use DL to provide better learning and utilize machine learning multilayer perceptron (MLP) as an intrusion detection (IDS) and intrusion prevention (IPS) system (IDPS) method. We deploy DL and MLP together as DLMLP. DLMLP improves the high detection of all intrusion attack features on the Internet of Things (IoT) device dataset, known as the CICIoT2023 dataset. We reference the CICIoT2023 dataset from the Canadian Institute of Cybersecurity (CIC) IoT device dataset. Our proposed method, the deep learning multilayer perceptron intrusion detection and prevention system model (DLMIDPSM), provides IDPST (intrusion detection and prevention system topology) capability. We use our proposed IDPST to capture, analyze, and prevent all intrusion attacks in the dataset. Moreover, our proposed DLMIDPSM employs a combination of artificial neural networks, ANNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Consequently, this project aims to develop a robust real-time intrusion detection and prevention system model. DLMIDPSM can predict, detect, and prevent intrusion attacks in the CICIoT2023 IoT dataset, with a high accuracy of above 85% and a high precision rate of 99%. Comparing the DLMIDPSM to the other literature, deep learning models and machine learning (ML) models have used decision tree (DT) and support vector machine (SVM), achieving a detection and prevention rate of 81% accuracy with only 72% precision. Furthermore, this research project breaks new ground by incorporating combined machine learning and deep learning models with IDPS capability, known as ML and DLMIDPSMs. We train, validate, or test the ML and DLMIDPSMs on the CICIoT2023 dataset, which helps to achieve higher accuracy and precision than the other deep learning models discussed above. Thus, our proposed combined ML and DLMIDPSMs achieved higher intrusion detection and prevention based on the confusion matrix’s high-rate attack detection and prevention values.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".