AI Based Intrusion Detection System for IIoT Using Deep Learning & XAI
Short Overview:
Secure Industrial Internet of Things (IIoT) networks with this advanced AI based intrusion detection system. Designed for students, researchers, and cybersecurity enthusiasts, this project leverages Convolutional Neural Networks (CNN) and Explainable AI (XAI) techniques to detect and classify network anomalies with over 99% accuracy using the real-world ToN-IoT dataset.
Key Features:
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Robust AI based intrusion detection tailored for IIoT environments
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High accuracy anomaly detection using CNN and ensemble models
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Transparent decision-making with SHAP & LIME (Explainable AI)
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Real-world ToN-IoT dataset for diverse attack scenarios
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Real-time alerts and detailed reporting for cybersecurity analysis
What You Will Get:
When you purchase this project, you receive:
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✅ Fully commented source code ready to run
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✅ Project documentation PDF covering architecture, dataset, model training, evaluation, and XAI integration
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✅ Preprocessed data and references to the ToN-IoT dataset
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✅ Trained model files for testing and validation
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✅ Step-by-step execution guide to run the system
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✅ Evaluation metrics and sample results for better understanding
Project Modules / Components:
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Data collection & preprocessing
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Feature extraction & engineering
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Deep Learning model (CNN) training
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Ensemble learning for improved accuracy
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Explainable AI integration (SHAP & LIME)
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Real-time anomaly detection and alerts
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Performance evaluation & reporting
Technologies Used:
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Python programming language
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Deep learning libraries: TensorFlow / PyTorch
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Data analysis libraries: NumPy, Pandas, Scikit-learn
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Explainable AI: SHAP & LIME
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Dataset: ToN-IoT
System Requirements:
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OS: Windows or Linux
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RAM: Minimum 8 GB recommended
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Python 3.8+
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Required Python libraries (listed in documentation)
Intended For:
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Final-year Engineering, BCA, MCA students
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Cybersecurity learners and researchers
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Academic and research projects on IIoT security
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Professionals exploring AI-based IDS solutions
Usage / Application Areas:
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Academic project submission
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Learning and implementing AI based intrusion detection
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Research and experimentation in IIoT network security
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Proof-of-concept for enterprise IIoT security solutions
What Is NOT Included:
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❌ Hardware components
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❌ Live deployment or cloud hosting
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❌ Commercial licensing
Support & Assistance:
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Step-by-step documentation included
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Setup and execution guidance available
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Technical clarification support for academic use
License / Disclaimer:
This project is intended solely for educational and research purposes. Use responsibly and do not deploy in commercial environments without proper licensing.
Why Choose This Project:
This package is complete, reliable, and tested with real-world IIoT data. The integration of AI based intrusion detection with XAI ensures both high accuracy and transparency, making it a trusted choice for learning, research, and experimentation in IIoT cybersecurity.
Get full access now to the source code, documentation, and trained models for a robust AI based intrusion detection system and elevate your IIoT security projects to the next level.
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