AI-Driven Intrusion Detection System (IDS)

Enhancing cybersecurity with LSTM-based real-time threat detection.

Problem Statement: Traditional IDS struggle with evolving attack patterns, high false positives, and lack adaptability in dynamic network environments. Our AI-powered IDS using LSTM overcomes these limitations with deep learning-based threat detection.

AI-Powered Intrusion Detection System

Milestones Achieved

Binary Classification Accuracy (99.32%):
Effectively differentiates between malicious and normal network traffic.
Multiclass Classification Accuracy (98.00%)
Detects and categorizes multiple attack types (DoS, Exploits, Reconnaissance, etc.).
Deep Learning-Based Threat Detection
Uses LSTM networks to analyze sequential network traffic patterns.
Real-Time Intrusion Detection
Optimized for fast inference, enabling live network monitoring.
Advanced Data Preprocessing
Converts categorical network data and missing values into a structured format for deep learning.
Robust Performance Metrics
Evaluated using Precision, Recall, F1-Score, and AUC-ROC curves.

Techniques Used

Long Short-Term Memory (LSTM): Models sequential network traffic patterns for anomaly detection.

Feature Engineering & Label Encoding: Converts categorical features into numerical representations.

Multi-Output Model Training: Simultaneously predicts binary and multiclass attack categories.

Fine-Tuning & Hyperparameter Optimization: Optimized model with dropout layers, batch size tuning, and learning rate adjustments.

Automated Model Training Pipeline: Streamlined training, evaluation, and model saving for easy deployment.

Performance Visualization: Plotted loss and accuracy graphs to track model improvements over epochs.

Strengthen Your Cybersecurity with AI-Powered IDS

Detect and prevent cyber threats with high accuracy using deep learning.

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