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https://github.com/aaaastark/intrusion-detection-system-mqtt-enabled-iot
Intrusion Detection System for MQTT Enabled IoT.
https://github.com/aaaastark/intrusion-detection-system-mqtt-enabled-iot
artifical-neural-network binary-classification classification-report data-processing data-visualization deep-learning feature-extraction intrusion-detection-system keras-tensorflow mean-absolute-error mean-squared-error mqtt mqtt-protocol mqtt-server multi-class-classification r2-score root-mean-square-error sklearn
Last synced: about 1 month ago
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Intrusion Detection System for MQTT Enabled IoT.
- Host: GitHub
- URL: https://github.com/aaaastark/intrusion-detection-system-mqtt-enabled-iot
- Owner: aaaastark
- License: mit
- Created: 2022-08-15T10:42:35.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-01T18:41:09.000Z (over 1 year ago)
- Last Synced: 2024-11-15T06:36:21.180Z (3 months ago)
- Topics: artifical-neural-network, binary-classification, classification-report, data-processing, data-visualization, deep-learning, feature-extraction, intrusion-detection-system, keras-tensorflow, mean-absolute-error, mean-squared-error, mqtt, mqtt-protocol, mqtt-server, multi-class-classification, r2-score, root-mean-square-error, sklearn
- Homepage: https://github.com/aaaastark/Intrusion-Detection-System-MQTT-Enabled-IoT
- Size: 1.55 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Research Paper: A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT
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>In this thesis, we proposed a Artificial Neural Network (ANN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Deep Neural Network (DNN), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using publicly available datasets, including MQTT-IoTIDS2020. The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the ANN-based model achieved 99.79%, 99.85%, and 94.36% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 99.71%, 99.70%, and 92.86%, respectively.
#### Research Paper: A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT
- Website Link for Paper: .
#### Loading the dataset IEEE DataPort (MQTT-IOT-IDS2020)
- Website Link for Dataset: .
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#### MQTT INTERNET OF THINGS INTRUSION DETECTION DATASET
#### Workflow that is used in this Project
- Data Processing
- Data Normalization
- Binary Class Classification
- Multi Class Classification
- Feature Extraction (BC and MC)
- Artificial Neural Network Model (ANN)
- Visualization Accuracy and Loss
- Classification Report
- Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R2 Score
#### APIs that are used in this Project
- tensorflow
- sklearn
- keras
- matplotlib
- numpy
- pandas
## Paper vs Proposed Results: DNN vs ANN netowrks
#### Binary Class Classification
- Bi-flow Features Paper: 99.75%
- Bi-flow Features Proposed: 99.85%
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- Ui-flow Features Paper: 99.92%
- Ui-flow Features Paper: 99.79%
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- Packet-flow Features Paper: 94.94%
- Packet-flow Features Paper: 94.36%
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#### Multi Class Classification
- Bi-flow Features Paper: 98.12%
- Bi-flow Features Proposed: 99.70%
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- Ui-flow Features Paper: 97.08%
- Ui-flow Features Paper: 99.71%
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- Packet-flow Features Paper: 90.79%
- Packet-flow Features Paper: 92.86%
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